4.6k citations · 7.2k across the 13 of their papers we have counts for
7 papers · 1 filter
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim, Joohwan Ko, Taeyoung Yun +6
GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can introduce…
Delta-AI: Local objectives for amortized inference in sparse graphical models
Jean-Pierre Falet, Hae Beom Lee, Esmeralda S. Whitammer +6
We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call -amortized inference (-AI). Our approach is based on the obs…
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
Aniket Didolkar, Kshitij Gupta, Anirudh Goyal +4
Recurrent neural networks have a strong inductive bias towards learning temporally compressed representations, as the entire history of a sequence is represented by a single vector…
Maximum Entropy Generators for Energy-Based Models
Rithesh Kumar, Sherjil Ozair, Anirudh Goyal +2
Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In this work, we propose learning both the e…
Twin Networks: Matching the Future for Sequence Generation
Dmitriy Serdyuk, Nan Rosemary Ke, Alessandro Sordoni +3
We propose a simple technique for encouraging generative RNNs to plan ahead. We train a "backward" recurrent network to generate a given sequence in reverse order, and we encourage…
Clustering is Efficient for Approximate Maximum Inner Product Search
Alex Auvolat, Sarath Chandar, Pascal Vincent +2
Efficient Maximum Inner Product Search (MIPS) is an important task that has a wide applicability in recommendation systems and classification with a large number of classes. Soluti…