most citedMutual Information State Intrinsic Control

3 citations · 4 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2021

Differentiable Architecture Pruning for Transfer Learning

Nicolo Colombo, Yang Gao

We propose a new gradient-based approach for extracting sub-architectures from a given large model. Contrarily to existing pruning methods, which are unable to disentangle the netw…

eess.AS2021

Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio

Gokce Keskin, Minhua Wu, Brian King +5

Automatic speech recognition (ASR) models are typically designed to operate on a single input data type, e.g. a single or multi-channel audio streamed from a device. This design de…

eess.AS2021

Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition

Bhargav Pulugundla, Yang Gao, Brian King +5

Attention-based beamformers have recently been shown to be effective for multi-channel speech recognition. However, they are less capable at capturing local information. In this wo…

cs.LG2021

Adapting by Pruning: A Case Study on BERT

Yang Gao, Nicolo Colombo, Wei Wang

Adapting pre-trained neural models to downstream tasks has become the standard practice for obtaining high-quality models. In this work, we propose a novel model adaptation paradig…

cs.LG20213 cited

Mutual Information State Intrinsic Control

Rui Zhao, Yang Gao, Pieter Abbeel +2

Reinforcement learning has been shown to be highly successful at many challenging tasks. However, success heavily relies on well-shaped rewards. Intrinsically motivated RL attempts…

cs.LG20201 cited

Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification

Nicolo Colombo, Yang Gao

The history of deep learning has shown that human-designed problem-specific networks can greatly improve the classification performance of general neural models. In most practical…