pdf
stringlengths 49
199
⌀ | aff
stringlengths 1
1.36k
⌀ | year
stringclasses 19
values | technical_novelty_avg
float64 0
4
⌀ | video
stringlengths 21
47
⌀ | doi
stringlengths 31
63
⌀ | presentation_avg
float64 0
4
⌀ | proceeding
stringlengths 43
129
⌀ | presentation
stringclasses 796
values | sess
stringclasses 576
values | technical_novelty
stringclasses 700
values | arxiv
stringlengths 10
16
⌀ | author
stringlengths 1
1.96k
⌀ | site
stringlengths 37
191
⌀ | keywords
stringlengths 2
582
⌀ | oa
stringlengths 86
198
⌀ | empirical_novelty_avg
float64 0
4
⌀ | poster
stringlengths 57
95
⌀ | openreview
stringlengths 41
45
⌀ | conference
stringclasses 11
values | corr_rating_confidence
float64 -1
1
⌀ | corr_rating_correctness
float64 -1
1
⌀ | project
stringlengths 1
162
⌀ | track
stringclasses 3
values | rating_avg
float64 0
10
⌀ | rating
stringlengths 1
17
⌀ | correctness
stringclasses 809
values | slides
stringlengths 32
41
⌀ | title
stringlengths 2
192
⌀ | github
stringlengths 3
165
⌀ | authors
stringlengths 7
161
⌀ | correctness_avg
float64 0
5
⌀ | confidence_avg
float64 0
5
⌀ | status
stringclasses 22
values | confidence
stringlengths 1
17
⌀ | empirical_novelty
stringclasses 763
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
task clustering;matrix completion;multi-task learning;few-shot learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Robust Task Clustering for Deep and Diverse Multi-Task and Few-Shot Learning
| null | null | 0 | 4 |
Withdraw
|
4;4;4
| null |
null |
Department of Electronics and Computer Science, University of Southampton
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yan Zhang, Jonathon Hare, Adam Prugel-Bennett
|
https://iclr.cc/virtual/2018/poster/307
|
visual question answering;vqa;counting
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
Learning to Count Objects in Natural Images for Visual Question Answering
|
https://github.com/Cyanogenoid/vqa-counting
| null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
THINK VISUALLY: QUESTION ANSWERING THROUGH VIRTUAL IMAGERY
| null | null | 0 | 0 |
Active
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpretability;generative adversarial networks
| null | 0 | null | null |
iclr
| -0.240192 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
Thinking like a machine — generating visual rationales through latent space optimization
| null | null | 0 | 3 |
Reject
|
3;4;2
| null |
null |
University of British Columbia
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Glen Berseth, Cheng Xie, Paul Cernek, Michiel van de Panne
|
https://iclr.cc/virtual/2018/poster/300
|
Reinforcement Learning;Distillation;Transfer Learning;Continual Learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Carnegie Mellon University; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
squad;stanford question answering dataset;reading comprehension;attention;text convolutions;question answering
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
| null | null | 0 | 4 |
Poster
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Non-convex optimization;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.333333 |
4;6;6
| null | null |
No Spurious Local Minima in a Two Hidden Unit ReLU Network
| null | null | 0 | 3 |
Workshop
|
4;3;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
boosting learning;deep learning;neural network
| null | 0 | null | null |
iclr
| -0.960769 | 0 | null |
main
| 4.333333 |
2;5;6
| null | null |
Deep Boosting of Diverse Experts
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;safe exploration;dqn
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Avoiding Catastrophic States with Intrinsic Fear
| null | null | 0 | 4 |
Reject
|
4;5;3
| null |
null |
Columbia University, New York, NY 10027, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Christopher Cueva, Xue-Xin Wei
|
https://iclr.cc/virtual/2018/poster/245
|
recurrent neural network;grid cell;neural representation of space
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8.333333 |
8;8;9
| null | null |
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Microsoft Research Montreal; Montréal Institute for Learning Algorithms (MILA), Université de Montréal, CIFAR Senior Fellow; Montréal Institute for Learning Algorithms (MILA), Université de Montréal, Work done while author was an intern at Microsoft Research Montreal; Montréal Institute for Learning Algorithms (MILA), Ecole Polytechnique de Montréal
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sandeep Subramanian, Adam Trischler, Yoshua Bengio, Christopher Pal
|
https://iclr.cc/virtual/2018/poster/99
|
distributed sentence representations;multi-task learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
4;8;8
| null | null |
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
| null | null | 0 | 5 |
Poster
|
5;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
clustering;deep learning;neural networks
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 2.666667 |
2;3;3
| null | null |
Clustering with Deep Learning: Taxonomy and New Methods
| null | null | 0 | 4.666667 |
Reject
|
5;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
NLU;word embeddings;representation learning
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Learning to Compute Word Embeddings On the Fly
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
program synthesis;program induction;example selection
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning to select examples for program synthesis
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Anonymous
| null |
Deep Learning;Autoencoders;Alternating Optimization
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Training Autoencoders by Alternating Minimization
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
UC Berkeley, Department of Electrical Engineering and Computer Science
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel
|
https://iclr.cc/virtual/2018/poster/64
|
meta-learning;few-shot learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
A Simple Neural Attentive Meta-Learner
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein
|
https://iclr.cc/virtual/2018/poster/91
|
Gaussian process;Bayesian regression;deep networks;kernel methods
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Deep Neural Networks as Gaussian Processes
| null | null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;mult-agent systems
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Federated Learning: Strategies for Improving Communication Efficiency
| null | null | 0 | 4.333333 |
Reject
|
3;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
SVM;siamese network;one-shot learning;few-shot learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Make SVM great again with Siamese kernel for few-shot learning
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
autonomous lane changing;decision making;deep reinforcement learning;q-learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
3;3;3
| null | null |
Tactical Decision Making for Lane Changing with Deep Reinforcement Learning
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null |
The University of Tokyo, RIKEN; The University of Tokyo
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada
|
https://iclr.cc/virtual/2018/poster/259
|
sound recognition;supervised learning;feature learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
4;8;9
| null | null |
Learning from Between-class Examples for Deep Sound Recognition
|
https://github.com/mil-tokyo/bc_learning_sound/
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Redwood Center for Theoretical Neuroscience, University of California, Berkeley
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Alexander Anderson, Cory P Berg
|
https://iclr.cc/virtual/2018/poster/244
|
Binary Neural Networks;Neural Network Visualization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6 |
4;7;7
| null | null |
The High-Dimensional Geometry of Binary Neural Networks
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
Department of Applied Mathematics and Statistics, Johns Hopkins University; Department of Computer Science, Johns Hopkins University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee
|
https://iclr.cc/virtual/2018/poster/155
|
expressive power;benefits of depth;empirical risk minimization;global optimality;computational hardness;combinatorial optimization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Understanding Deep Neural Networks with Rectified Linear Units
| null | null | 0 | 4.333333 |
Poster
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Multi-Agent Reinforcement Learning;StarCraft Micromanagement Tasks
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Revisiting The Master-Slave Architecture In Multi-Agent Deep Reinforcement Learning
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;Structured Prediction;Natural Language Processing;Neural Program Synthesis
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Neural Program Search: Solving Data Processing Tasks from Description and Examples
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null |
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Abram Friesen, Pedro Domingos
|
https://iclr.cc/virtual/2018/poster/92
|
hard-threshold units;combinatorial optimization;target propagation;straight-through estimation;quantization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
domain adaptation;neural networks;generative models;discriminative models
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Principled Hybrids of Generative and Discriminative Domain Adaptation
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
style transfer;text generation;non-parallel data
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Language Style Transfer from Non-Parallel Text with Arbitrary Styles
| null | null | 0 | 0 |
Withdraw
| null | null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep reinforcement learning;task execution;instruction execution
| null | 0 | null | null |
iclr
| -0.5 | 0 |
https://youtu.be/e_ZXVS5VutM
|
main
| 5.333333 |
4;6;6
| null | null |
Neural Task Graph Execution
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
Preferred Networks, Inc.; Ritsumeikan University; National Institute of Informatics
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
|
https://iclr.cc/virtual/2018/poster/331
|
Generative Adversarial Networks;Deep Generative Models;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Spectral Normalization for Generative Adversarial Networks
|
https://github.com/pfnet-research/sngan_projection
| null | 0 | 3 |
Oral
|
4;2;3
| null |
null |
Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Pan Zhou, Jiashi Feng, Pan Zhou
|
https://iclr.cc/virtual/2018/poster/329
|
Deep Learning Analysis;Deep Learning Theory;Empirical Risk;Landscape Analysis;Nonconvex Optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Empirical Risk Landscape Analysis for Understanding Deep Neural Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
MPI for Intelligent Systems; University of Amsterdam; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schoelkopf
|
https://iclr.cc/virtual/2018/poster/76
|
fidelity-weighted learning;semisupervised learning;weakly-labeled data;teacher-student
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Fidelity-Weighted Learning
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Distributional shift;causal effects;domain adaptation
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Learning Weighted Representations for Generalization Across Designs
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Samuel Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V Le
|
https://iclr.cc/virtual/2018/poster/272
|
batch size;learning rate;simulated annealing;large batch training;scaling rules;stochastic gradient descent;sgd;imagenet;optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Don't Decay the Learning Rate, Increase the Batch Size
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Bayesian Deep Learning;Amortized Inference;Variational Auto-Encoders;Learning to Learn
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Learning to Infer
| null | null | 0 | 4.333333 |
Workshop
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;medical;records;time;series;generation;privacy
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, Debbie Marr
|
https://iclr.cc/virtual/2018/poster/208
|
Low precision;binary;ternary;4-bits networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
5;5;9
| null | null |
WRPN: Wide Reduced-Precision Networks
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Accelerator Architecture Lab, Intel Labs
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Asit Mishra, Debbie Marr
|
https://iclr.cc/virtual/2018/poster/173
|
Ternary;4-bits;low precision;knowledge distillation;knowledge transfer;model compression
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Xu He, Herbert Jaeger
|
https://iclr.cc/virtual/2018/poster/233
|
Catastrophic Interference;Conceptor;Backpropagation;Continual Learning;Lifelong Learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation
| null | null | 0 | 3.666667 |
Poster
|
3;3;5
| null |
null |
Paper under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reading Comprehension;Answering Multiple Choice Questions
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
Princeton University; Columbia University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sanjeev Arora, Mikhail Khodak, Nikunj Umesh Saunshi, Kiran Vodrahalli
|
https://iclr.cc/virtual/2018/poster/96
|
theory;LSTM;unsupervised learning;word embeddings;compressed sensing;sparse recovery;document representation;text classification
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs
| null | null | 0 | 2.666667 |
Poster
|
4;3;1
| null |
null |
The University of Melbourne, Parkville, Australia; National Institute of Informatics, Tokyo, Japan; University of Michigan, Ann Arbor, USA; Tsinghua University, Beijing, China; University of California, Berkeley, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Xingjun Ma, Bo Li, Yisen Wang, Sarah Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, James Bailey
|
https://iclr.cc/virtual/2018/poster/328
|
Adversarial Subspace;Local Intrinsic Dimensionality;Deep Neural Networks
| null | 0 | null | null |
iclr
| 0.654654 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
| null | null | 0 | 2.666667 |
Oral
|
1;4;3
| null |
null |
Department of Computer Science, University of Bonn, Germany; Fraunhofer Institute IAIS, Sankt Augustin, Germany
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Henning Petzka, Asja Fischer, Denis Lukovnikov
|
https://iclr.cc/virtual/2018/poster/17
| null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
2;6;7
| null | null |
On the regularization of Wasserstein GANs
| null | null | 0 | 3.666667 |
Poster
|
2;5;4
| null |
null |
Robotics Institute, Carnegie Mellon University; Volvo Construction Equipment, Volvo Group
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M Kitani
|
https://iclr.cc/virtual/2018/poster/132
|
Deep learning;Neural networks;Model compression
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
4;5;9
| null | null |
N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
inversion scheme;deep neural networks;semi-supervised learning;MNIST;SVHN;CIFAR10
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Semi-Supervised Learning via New Deep Network Inversion
| null | null | 0 | 3.666667 |
Reject
|
5;4;2
| null |
null |
Courant Institute of Mathematical Sciences, Center for Data Science, New York, NY 10012, USA; Courant Institute of Mathematical Sciences, Center for Data Science, New York University, New York, NY 10012, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Alex Nowak, David Folqué Garcia, Joan Bruna
|
https://iclr.cc/virtual/2018/poster/44
|
Neural Networks;Combinatorial Optimization;Algorithms
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Divide and Conquer Networks
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Word Embeddings;Tensor Factorization;Natural Language Processing
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
LEARNING SEMANTIC WORD RESPRESENTATIONS VIA TENSOR FACTORIZATION
| null | null | 0 | 4.333333 |
Reject
|
3;5;5
| null |
null |
†The University of Hong Kong; ‡Salesforce Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jiatao Gu, James Bradbury, Caiming Xiong, Victor OK Li, richard socher
|
https://iclr.cc/virtual/2018/poster/241
|
machine translation;non-autoregressive;transformer;fertility;nmt
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Non-Autoregressive Neural Machine Translation
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
UC Irvine; Amazon.com; Snap Inc; UC Santa Barbara; Think Big Analytics
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Recommender systems;deep learning;personalization
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
null |
N/A
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
vocabulary-informed learning;data augmentation
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
structured prediction;RAML;theory;Bayes decision rule;reward function
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML
| null | null | 0 | 3 |
Reject
|
4;2;3
| null |
null |
The Institute for Theoretical Computer Science, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China; Department of Computer Science, University of Southern California, Los Angeles, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jiayuan Mao, Honghua Dong, Joseph J Lim
|
https://iclr.cc/virtual/2018/poster/3
|
reinforcement learning;transfer learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Universal Agent for Disentangling Environments and Tasks
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Stanford University; Google AI Perception; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Daniel Levy, Matthew D Hoffman, Jascha Sohl-Dickstein
|
https://iclr.cc/virtual/2018/poster/284
|
markov;chain;monte;carlo;sampling;posterior;deep;learning;hamiltonian;mcmc
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Generalizing Hamiltonian Monte Carlo with Neural Networks
|
https://github.com/google-research/google-research/tree/master/generalizing_hmc
| null | 0 | 3 |
Poster
|
3;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning Gaussian Policies from Smoothed Action Value Functions
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
Department of Computer Science and Engineering, Indian Institute of Technology, Madras; Department of Mechanical Engineering, Indian Institute of Technology, Madras; Department of Electrical Engineering, Indian Institute of Technology, Madras; Department of Computer Science and Engineering, and Robert Bosch Centre for Data Science and AI (RBC-DSAI), Indian Institute of Technology, Madras
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sahil Sharma, Ashutosh Kumar Jha, Parikshit Hegde, Balaraman Ravindran
|
https://iclr.cc/virtual/2018/poster/257
|
Deep Reinforcement Learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Learning to Multi-Task by Active Sampling
| null | null | 0 | 3.666667 |
Poster
|
3;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
forward modeling;partially observable;deep learning;strategy game;real-time strategy
| null | 0 | null | null |
iclr
| 0.944911 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
| null | null | 0 | 2.666667 |
Reject
|
1;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
label noise;weakly supervised learning;robustness of neural networks;deep learning;large datasets
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Deep Learning is Robust to Massive Label Noise
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Deconvolutional Layer;Pixel CNN
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Pixel Deconvolutional Networks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Word embedding;tensor decomposition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Learning Covariate-Specific Embeddings with Tensor Decompositions
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
LVCSR;speech recognition;embedded;low rank factorization;RNN;GRU;trace norm
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Trace norm regularization and faster inference for embedded speech recognition RNNs
|
https://github.com/paddlepaddle/farm
| null | 0 | 3.666667 |
Reject
|
3;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial attacks;security;auto-encoder
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
LatentPoison -- Adversarial Attacks On The Latent Space
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural language processing;background knowledge;word embeddings;question answering;natural language inference
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Dynamic Integration of Background Knowledge in Neural NLU Systems
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Manifold Learning;Non-linear Dimensionality Reduction;Neural Networks;Unsupervised Learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Parametric Manifold Learning Via Sparse Multidimensional Scaling
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Attribute-aware Collaborative Filtering: Survey and Classification
| null | null | 0 | 4.666667 |
Withdraw
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
price predictions;expert system;recurrent neural networks;deep learning;natural language processing
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null |
Universit´e de Bretagne Sud, IRISA, UMR 6074, CNRS; Kyoto University, Graduate School of Informatics; NTT Communication Science Laboratories; Universit´e Cˆote d'Azur, Lagrange, UMR 7293, CNRS, OCA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel
|
https://iclr.cc/virtual/2018/poster/179
|
optimal transport;Wasserstein;domain adaptation;generative models;Monge map;optimal mapping
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.75 |
6;6;7;8
| null | null |
Large Scale Optimal Transport and Mapping Estimation
| null | null | 0 | 3 |
Poster
|
3;3;3;3
| null |
null |
Google
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang
|
https://iclr.cc/virtual/2018/poster/187
|
differential privacy;LSTMs;language models;privacy
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Learning Differentially Private Recurrent Language Models
| null | null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Learning what to learn in a neural program
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Multimodal Sentiment Analysis To Explore the Structure of Emotions
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
RNNs
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
Efficiently applying attention to sequential data with the Recurrent Discounted Attention unit
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement Learning;Imitation Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Faster Reinforcement Learning with Expert State Sequences
| null | null | 0 | 4 |
Reject
|
5;4;3
| null |
null |
Department of Engineering Science, University of Oxford; Department of Statistics, University of Oxford
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood
|
https://iclr.cc/virtual/2018/poster/31
|
Variational Autoencoders;Inference amortization;Model learning;Sequential Monte Carlo;ELBOs
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.666667 |
3;7;7
| null | null |
Auto-Encoding Sequential Monte Carlo
| null | null | 0 | 3 |
Poster
|
2;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
supervised learning;unsupervised learning;self-organization;internal representation;topological structure
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 2.666667 |
2;2;4
| null | null |
Self-Organization adds application robustness to deep learners
| null | null | 0 | 4.666667 |
Withdraw
|
5;5;4
| null |
null |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology; Paul G. Allen School of Computer Science & Engineering, University of Washington
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yonatan Belinkov, Yonatan Bisk
|
https://iclr.cc/virtual/2018/poster/172
|
neural machine translation;characters;noise;adversarial examples;robust training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
Synthetic and Natural Noise Both Break Neural Machine Translation
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Reinforcement Learning;Domain Adaptation;Adversarial Networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
linear quadratic regulator;policy gradient;natural gradient;reinforcement learning;non-convex optimization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Global Convergence of Policy Gradient Methods for Linearized Control Problems
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null |
Under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
adversarial examples
| null | 0 | null | null |
iclr
| -0.944911 | 0 |
https://youtu.be/YXy6oX1iNoA
|
main
| 6.333333 |
5;6;8
| null | null |
Synthesizing Robust Adversarial Examples
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null |
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Murat Kocaoglu, Christopher Snyder, Alexandros Dimakis, Sriram Vishwanath
|
https://iclr.cc/virtual/2018/poster/159
|
causality;structural causal models;GANs;conditional GANs;BEGAN;adversarial training
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
Racah Institute of Physics, The Hebrew University of Jerusalem; Department of Engineering Science, University of Oxford; Assistant Professor at the Federal University of Rio Grande, Rio Grande, Brazil
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Zohar Ringel, Rodrigo Andrade de Bem
|
https://iclr.cc/virtual/2018/poster/303
|
Deep Convolutional Networks;Loss function landscape;Graph Structured Data;Training Complexity;Theory of deep learning;Percolation theory;Anderson Localization
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Critical Percolation as a Framework to Analyze the Training of Deep Networks
| null | null | 0 | 2.333333 |
Poster
|
1;3;3
| null |
null |
Microsoft Business AI and Research, National Taiwan University; Microsoft Business AI and Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen
|
https://iclr.cc/virtual/2018/poster/246
|
Attention Mechanism;Machine Comprehension;Natural Language Processing;Deep Learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension
| null | null | 0 | 4 |
Poster
|
5;4;3
| null |
null |
Department of Computer Science and Operations Research, University of Montréal, Canada; Department of Computer Science, Stanford University, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;auto-encoders;3D point clouds;generative models;GANs;Gaussian Mixture Models
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Learning Representations and Generative Models for 3D Point Clouds
| null | null | 0 | 4.666667 |
Workshop
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
VAE;Generative Model;Vision;Natural Language
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;5;5
| null | null |
Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
learning from demonstration;reinforcement learning;maximum entropy learning
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
Reinforcement Learning from Imperfect Demonstrations
| null | null | 0 | 4 |
Workshop
|
3;4;5
| null |
null |
Washington State University, Pullman; NEC Laboratories America
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Bo Zong, Qi Song, Martin Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen
|
https://iclr.cc/virtual/2018/poster/126
|
Density estimation;unsupervised anomaly detection;high-dimensional data;Deep autoencoder;Gaussian mixture modeling;latent low-dimensional space
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep neural networks;short text classification;cybersecurity;domain generation algorithms;malicious domain names
| null | 0 | null | null |
iclr
| 0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Character Level Based Detection of DGA Domain Names
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hierarchical;tree-lstm;treelstm;syntax;composition
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Xu Chen, Jiang Wang, Hao Ge
|
https://iclr.cc/virtual/2018/poster/273
|
GAN;Primal-Dual Subgradient;Mode Collapse;Saddle Point
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE ON GAN
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
Simon Fraser University, Burnaby, BC, Canada; Microsoft Research, Cambridge, UK
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
|
https://iclr.cc/virtual/2018/poster/216
|
programs;source code;graph neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 8 |
8;8;8
| null | null |
Learning to Represent Programs with Graphs
| null | null | 0 | 4 |
Oral
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimization;vanishing gradients;shattered gradients;skip-connections
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Avoiding degradation in deep feed-forward networks by phasing out skip-connections
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null |
Carnegie Mellon University; DeepMind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu
|
https://iclr.cc/virtual/2018/poster/45
|
deep learning;architecture search
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;6;8
| null | null |
Hierarchical Representations for Efficient Architecture Search
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null |
The University of Tokyo
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Raphael Shu, Hideki Nakayama
|
https://iclr.cc/virtual/2018/poster/242
|
natural language processing;word embedding;compression;deep learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Compressing Word Embeddings via Deep Compositional Code Learning
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
The Context-Aware Learner
| null | null | 0 | 4 |
Reject
|
4;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Few-Shot Learning;Neural Network Understanding;Visual Concepts
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Unleashing the Potential of CNNs for Interpretable Few-Shot Learning
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L Griffiths
|
https://iclr.cc/virtual/2018/poster/313
|
meta-learning;learning to learn;hierarchical Bayes;approximate Bayesian methods
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein
|
https://iclr.cc/virtual/2018/poster/291
|
deep learning;pruning;LSTM;convolutional networks;recurrent neural network;sparse networks;neuromorphic hardware;energy efficient computing;low memory hardware;stochastic differential equation;fokker-planck equation
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Deep Rewiring: Training very sparse deep networks
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
3D fMRI data;Deep Learning;Generative Adversarial Network;Classification
| null | 0 | null | null |
iclr
| 0.981981 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Hallucinating brains with artificial brains
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph topology;GAN;network science;hierarchical learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Graph Topological Features via GAN
| null | null | 0 | 4.333333 |
Reject
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null |
Sanjeev Arora, Andrej Risteski, Yi Zhang
|
https://iclr.cc/virtual/2018/poster/72
|
Generative Adversarial Networks;mode collapse;birthday paradox;support size estimation
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Do GANs learn the distribution? Some Theory and Empirics
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Machine learning;Neural networks;Sparse neural networks;Pre-defined sparsity;Scatter;Connectivity patterns;Adjacency matrix;Parameter Reduction;Morse code
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Characterizing Sparse Connectivity Patterns in Neural Networks
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.