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Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder
Guy Lorberbom, Andreea Gane, Tommi Jaakkola, Tamir Hazan
Models with discrete latent variables drive extensive research in machine learning applications, including language classification and generation [42, 11, 34], molecular synthesis [19], or game solving[25].
A-N E SI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
We study the problem of combining neural networks with symbolic reasoning.
Zero-shot Learning via Simultaneous Generating and Learning
Neural Information Processing Systems http://nips.cc/
Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models
Understanding the neural basis of behavior is a fundamental goal in neuroscience. Current research in large-scale neuro-behavioral data analysis often relies on decoding models, which quantify behavioral information in neural data but lack details on behavior encoding. This raises an intriguing scientific question: " how
b9e98316cb72fee82cc1160da5810abc-Paper-Conference.pdf
The constraint setX Rd1 is compact and convex.
Successor Feature Landmarksfor Long-Horizon Goal-Conditioned Reinforcement Learning
Planned Path Graph + SF Update Graph + 4. Use random policy to explore
HindsightCreditAssignment
A reinforcement learning (RL) agent is tasked with two fundamental, interdependent problems: exploration(howtodiscoverusefuldata),andcreditassignment(howtoincorporateit). The simplest way of estimating the value function is by averaging returns (futurediscountedsumsofrewards)startingfromtaking ainx.
Large Scale Adversarial Representation Learning
Neural Information Processing Systems http://nips.cc/