Search Results for author:
Found 11 papers, 5 papers with code
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
Automatic design of novel potential 3CL$^{\text{pro}}$ and PL$^{\text{pro}}$ inhibitors
With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-2, we propose the general molecule optimization framework, Molecular Neural Assay Search (MONAS), consisting of three components: a property predictor which identifies molecules with specific desirable properties, an energy model which approximates the statistical similarity of a given molecule to known training molecules, and a molecule search method.
Safe Interactive Model-Based Learning
A min-max control framework, based on alternate minimisation and backpropagation through the forward model, is used for the offline computation of the controller and the safe set.
Model-Based Active Exploration
Efficient exploration is an unsolved problem in Reinforcement Learning which is usually addressed by reactively rewarding the agent for fortuitously encountering novel situations.
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
This paper introduces Non-Autonomous Input-Output Stable Network(NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system.
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.
Kernel-based Information Criterion
This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis.
Deep Networks with Internal Selective Attention through Feedback Connections
It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters.
Ranked #198 on
Image Classification
on CIFAR-10
A Clockwork RNN
Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs.
Compete to Compute
Local competition among neighboring neurons is common in biological neural networks (NNs).
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data.
