Search Results for author:
Found 13 papers, 8 papers with code
Emergent Communication: Generalization and Overfitting in Lewis Games
Lewis signaling games are a class of simple communication games for simulating the emergence of language.
BYOL-Explore: Exploration by Bootstrapped Prediction
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments.
Broaden Your Views for Self-Supervised Video Learning
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data.
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
BYOL works even without batch statistics
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation.
Monte-Carlo Tree Search as Regularized Policy Optimization
The combination of Monte-Carlo tree search (MCTS) with deep reinforcement learning has led to significant advances in artificial intelligence.
Bootstrap your own latent: A new approach to self-supervised Learning
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
Ranked #2 on
Self-Supervised Person Re-Identification
on SYSU-30k
Representation Learning
Self-Supervised Image Classification
+3
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
These latent embeddings are themselves trained to be predictive of the aforementioned representations.
Planning in entropy-regularized Markov decision processes and games
We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the SmoothCruiser.
Optimistic optimization of a Brownian
Given $W$, our goal is to return an $\epsilon$-approximation of its maximum using the smallest possible number of function evaluations, the sample complexity of the algorithm.
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
We study the sampling-based planning problem in Markov decision processes (MDPs) that we can access only through a generative model, usually referred to as Monte-Carlo planning.
Black-box optimization of noisy functions with unknown smoothness
We study the problem of black-box optimization of a function $f$ of any dimension, given function evaluations perturbed by noise.
