30 citations · 63 across the 10 of their papers we have counts for
17 papers · 1 filter
Layer-Stack Temperature Scaling
Amr Khalifa, Michael C. Mozer, Hanie Sedghi +2
Recent works demonstrate that early layers in a neural network contain useful information for prediction. Inspired by this, we show that extending temperature scaling across all la…
Overcoming Temptation: Incentive Design For Intertemporal Choice
Shruthi Sukumar, Adrian F. Ward, Camden Elliott-Williams +2
Individuals are often faced with temptations that can lead them astray from long-term goals. We're interested in developing interventions that steer individuals toward making good…
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Dianbo Liu, Alex Lamb, Xu Ji +4
Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It has been theoretically and empirically sho…
Discrete-Valued Neural Communication
Dianbo Liu, Alex Lamb, Kenji Kawaguchi +4
Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed of positional elements, modular archite…
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan +1
A discriminatively trained neural net classifier can fit the training data perfectly if all information about its input other than class membership has been discarded prior to the…
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
Michael L. Iuzzolino, Michael C. Mozer, Samy Bengio
Although deep feedforward neural networks share some characteristics with the primate visual system, a key distinction is their dynamics. Deep nets typically operate in serial stag…