activity
20192022
most citedAddressing the Loss-Metric Mismatch with Adaptive Loss Alignment

22 citations · 30 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG2022

Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation

Martin Bertran, Walter Talbott, Nitish Srivastava +1

Learning generalizeable policies from visual input in the presence of visual distractions is a challenging problem in reinforcement learning. Recently, there has been renewed inter…

cs.LG2021

An Attention Free Transformer

Shuangfei Zhai, Walter Talbott, Nitish Srivastava +4

We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention. In an AFT layer, the key and value are…

cs.LG20202 cited

Set Distribution Networks: a Generative Model for Sets of Images

Shuangfei Zhai, Walter Talbott, Miguel Angel Bautista +2

Images with shared characteristics naturally form sets. For example, in a face verification benchmark, images of the same identity form sets. For generative models, the standard wa…

cs.LG20195 cited

Adversarial Fisher Vectors for Unsupervised Representation Learning

Shuangfei Zhai, Walter Talbott, Carlos Guestrin +1

We examine Generative Adversarial Networks (GANs) through the lens of deep Energy Based Models (EBMs), with the goal of exploiting the density model that follows from this formulat…

cs.LG201922 cited

Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment

Chen Huang, Shuangfei Zhai, Walter Talbott +4

In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric. In this work we assess this…