Search Results for author:
Found 17 papers, 13 papers with code
Recurrent Highway Networks
We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell.
Ranked #16 on
Language Modelling
on Hutter Prize
EvoTorch: Scalable Evolutionary Computation in Python
Evolutionary computation is an important component within various fields such as artificial intelligence research, reinforcement learning, robotics, industrial automation and/or optimization, engineering design, etc.
Training Very Deep Networks
Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success.
Ranked #34 on
Image Classification
on MNIST
Bayesian Flow Networks
Notably, the network inputs for discrete data lie on the probability simplex, and are therefore natively differentiable, paving the way for gradient-based sample guidance and few-step generation in discrete domains such as language modelling.
Ranked #3 on
Image Generation
on Binarized MNIST
Highway Networks
There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success.
LSTM: A Search Space Odyssey
Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995.
Training Agents using Upside-Down Reinforcement Learning
Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments.
ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution
In these algorithms, gradients of the total reward with respect to the policy parameters are estimated using a population of solutions drawn from a search distribution, and then used for policy optimization with stochastic gradient ascent.
Binding via Reconstruction Clustering
Disentangled distributed representations of data are desirable for machine learning, since they are more expressive and can generalize from fewer examples.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Reward-Weighted Regression Converges to a Global Optimum
Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework.
Multimeasurement Generative Models
We formally map the problem of sampling from an unknown distribution with a density in $\mathbb{R}^d$ to the problem of learning and sampling a smoother density in $\mathbb{R}^{Md}$ obtained by convolution with a fixed factorial kernel: the new density is referred to as M-density and the kernel as multimeasurement noise model (MNM).
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets
Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for given inputs in a dataset do not change over time.
ContextVP: Fully Context-Aware Video Prediction
Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions.
Understanding Locally Competitive Networks
Recently proposed neural network activation functions such as rectified linear, maxout, and local winner-take-all have allowed for faster and more effective training of deep neural architectures on large and complex datasets.
Learning Relative Return Policies With Upside-Down Reinforcement Learning
Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems.
Universal Smoothed Score Functions for Generative Modeling
We consider the problem of generative modeling based on smoothing an unknown density of interest in $\mathbb{R}^d$ using factorial kernels with $M$ independent Gaussian channels with equal noise levels introduced by Saremi and Srivastava (2022).
