160 citations · 246 across the 13 of their papers we have counts for
13 papers
LookThere! Sparse Vision by Reinforced Selection
Sreehari Rammohan, Yousef Yassin, Anthony Fuller +3
Vision transformers typically treat every image token as equally important, yet for most tasks in computer vision only a fraction are needed. Adaptive computation methods accelerat…
Temporal Difference Learning for Diffusion Models
Qizhen Ying, Yangchen Pan, Victor Adrian Prisacariu +1
Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between…
LookWhere? Efficient Visual Recognition by Learning Where to Look and What to See from Self-Supervision
Anthony Fuller, Yousef Yassin, Junfeng Wen +4
Vision transformers are ever larger, more accurate, and more expensive to compute. The expense is even more extreme at high resolution as the number of tokens grows quadratically w…
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models
Yangchen Pan, Junfeng Wen, Chenjun Xiao +1
In traditional statistical learning, data points are usually assumed to be independently and identically distributed (i.i.d.) following an unknown probability distribution. This pa…
Find Your Friends: Personalized Federated Learning with the Right Collaborators
Yi Sui, Junfeng Wen, Yenson Lau +2
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly…
A Parametric Class of Approximate Gradient Updates for Policy Optimization
Ramki Gummadi, Saurabh Kumar, Junfeng Wen +1
Approaches to policy optimization have been motivated from diverse principles, based on how the parametric model is interpreted (e.g. value versus policy representation) or how the…
Decentralized Federated Learning through Proxy Model Sharing
Shivam Kalra, Junfeng Wen, Jesse C. Cresswell +2
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework tha…
Characterizing the Gap Between Actor-Critic and Policy Gradient
Junfeng Wen, Saurabh Kumar, Ramki Gummadi +1
Actor-critic (AC) methods are ubiquitous in reinforcement learning. Although it is understood that AC methods are closely related to policy gradient (PG), their precise connection…
Batch Stationary Distribution Estimation
Junfeng Wen, Bo Dai, Lihong Li +1
We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions. Classical simulation-based approaches assume acc…
Universal Successor Features for Transfer Reinforcement Learning
Chen Ma, Dylan R. Ashley, Junfeng Wen +1
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et…
Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen, Russell Greiner, Dale Schuurmans
In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing chara…
Few-Shot Self Reminder to Overcome Catastrophic Forgetting
Junfeng Wen, Yanshuai Cao, Ruitong Huang
Deep neural networks are known to suffer the catastrophic forgetting problem, where they tend to forget the knowledge from the previous tasks when sequentially learning new tasks.…
Universal Successor Representations for Transfer Reinforcement Learning
Chen Ma, Junfeng Wen, Yoshua Bengio
The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynami…