908 citations · 2.7k across the 30 of their papers we have counts for
32 papers
DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution
Keshav Santhanam, Siddharth Krishna, Ryota Tomioka +2
The rapidly growing size of deep neural network (DNN) models and datasets has given rise to a variety of distribution strategies such as data, tensor-model, pipeline parallelism, a…
Regularized Policies are Reward Robust
Hisham Husain, Kamil Ciosek, Ryota Tomioka
Entropic regularization of policies in Reinforcement Learning (RL) is a commonly used heuristic to ensure that the learned policy explores the state-space sufficiently before overf…
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
Chen Liu, Mathieu Salzmann, Tao Lin +2
We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial…
On Certifying Non-uniform Bound against Adversarial Attacks
Chen Liu, Ryota Tomioka, Volkan Cevher
This work studies the robustness certification problem of neural network models, which aims to find certified adversary-free regions as large as possible around data points. In con…
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu, Charline Le Lan, Chris J. Maddison +2
The variational auto-encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, tradit…
Depth and nonlinearity induce implicit exploration for RL
Justas Dauparas, Ryota Tomioka, Katja Hofmann
The question of how to explore, i.e., take actions with uncertain outcomes to learn about possible future rewards, is a key question in reinforcement learning (RL). Here, we show a…