Tuesday, April 25, 2017

#ICLR2017 Tuesday Morning Program

So ICLR 2017 continues today in Toulon, there will be a blog post for each half day that features directly links to papers from the Open review section. The meeting will be featured live on Facebook here at: https://www.facebook.com/iclr.cc/ . If you want to say hi, I am around.and we're hiring.

7.30 – 9.00 Registration
9.00 - 9.40 Invited talk 1: Chloé-Agathe Azencott
9.40 - 10.00 Contributed talk 1: Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data - BEST PAPER AWARD
10.00 - 10.20 Contributed talk 2: Learning Graphical State Transitions
10.20 - 10.30 Coffee Break
10.30 - 12.30 Poster Session 1 (Conference Papers, Workshop Papers)

 Conference posters (1st floor)
C1: DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning (code)
C3: Deep Probabilistic Programming
C4: Lie-Access Neural Turing Machines
C5: Learning Features of Music From Scratch
C6: Mode Regularized Generative Adversarial Networks
C7: End-to-end Optimized Image Compression (web)
C8: Variational Recurrent Adversarial Deep Domain Adaptation
C9: Steerable CNNs
C10: Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning (code)
C11: PixelVAE: A Latent Variable Model for Natural Images
C12: A recurrent neural network without chaos
C13: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
C14: Tree-structured decoding with doubly-recurrent neural networks
C15: Introspection:Accelerating Neural Network Training By Learning Weight Evolution
C16: Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization (page)
C17: Quasi-Recurrent Neural Networks (Keras)
C18: Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain
C19: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
C20: Trusting SVM for Piecewise Linear CNNs
C21: Maximum Entropy Flow Networks
C22: The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
C23: Unrolled Generative Adversarial Networks
C24: A Simple but Tough-to-Beat Baseline for Sentence Embeddings (blog entry)
C25: Query-Reduction Networks for Question Answering (code)
C26: Machine Comprehension Using Match-LSTM and Answer Pointer (code)
C27: Words or Characters? Fine-grained Gating for Reading Comprehension
C28: Dynamic Coattention Networks For Question Answering (code)
C29: Multi-view Recurrent Neural Acoustic Word Embeddings
C30: Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement
C31: Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
C32: Generalizing Skills with Semi-Supervised Reinforcement Learning
C33: Improving Policy Gradient by Exploring Under-appreciated Rewards
3rd Floor
W1: Programming With a Differentiable Forth Interpreter
W2: Unsupervised Feature Learning for Audio Analysis
W3: Neural Functional Programming
W4: A Smooth Optimisation Perspective on Training Feedforward Neural Networks
W5: Synthetic Gradient Methods with Virtual Forward-Backward Networks
W6: Explaining the Learning Dynamics of Direct Feedback Alignment
W7: Training a Subsampling Mechanism in Expectation
W8: Deep Kernel Machines via the Kernel Reparametrization Trick
W9: Encoding and Decoding Representations with Sum- and Max-Product Networks
W10: Embracing Data Abundance
W11: Variational Intrinsic Control
W12: Fast Adaptation in Generative Models with Generative Matching Networks
W13: Efficient variational Bayesian neural network ensembles for outlier detection
W14: Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
W15: Adaptive Feature Abstraction for Translating Video to Language
W16: Delving into adversarial attacks on deep policies
W17: Tuning Recurrent Neural Networks with Reinforcement Learning
W18: DeepMask: Masking DNN Models for robustness against adversarial samples
W19: Restricted Boltzmann Machines provide an accurate metric for retinal responses to visual stimuli

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SeanVN said...

Re: https://openreview.net/pdf?id=HkXKUTVFl
I'm trying dropout in relation to the back error projection. Anyway there are tons of ideas to explore, especially if you start using fast random projection algorithms for both the back error projection and the forward aspects of a network.

SeanVN said...

I think what is happening is that you are getting unsupervised feature learning in the deeper layers and then one final readout layer. That may give a boost in performance in some circumstances. There probably are better ways to do unsupervised feature learning prior to a readout layer. There are also some aspects to do with noise and maybe some cooling effect over time as the system adapts. Thumbs up or thumbs down, I don't know. You be the judge.