32 citations · 74 across the 21 of their papers we have counts for
4 papers · 1 filter
Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs
Qiyuan Wu, Katie Z Luo, Bharath Hariharan +2
Trajectory forecasting for autonomous driving has advanced rapidly, yet representative models often produce uninformative posteriors over forecast modes, causing problems for mode…
RanDeS: Randomized Delta Superposition for Multi-Model Compression
Hangyu Zhou, Aaron Gokaslan, Volodymyr Kuleshov +1
From a multi-model compression perspective, model merging enables memory-efficient serving of multiple models fine-tuned from the same base, but suffers from degraded performance d…
Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning
Chia-Hsiang Kao, Bharath Hariharan
Despite its widespread use in neural networks, error backpropagation has faced criticism for its lack of biological plausibility, suffering from issues such as the backward locking…
On the Efficacy of Knowledge Distillation
Jang Hyun Cho, Bharath Hariharan
In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and its dependence on student and teacher architectures. Starting with the observation tha…