Search Results for author:
Found 12 papers, 9 papers with code
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science.
On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function.
Leveraging Synthetic Targets for Machine Translation
In this work, we provide a recipe for training machine translation models in a limited resource setting by leveraging synthetic target data generated using a large pre-trained model.
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.
From Points to Functions: Infinite-dimensional Representations in Diffusion Models
Consequently, it is reasonable to ask if there is an intermediate time step at which the preserved information is optimal for a given downstream task.
On Neural Architecture Inductive Biases for Relational Tasks
Recent work has explored how forcing relational representations to remain distinct from sensory representations, as it seems to be the case in the brain, can help artificial systems.
Is a Modular Architecture Enough?
Inspired from human cognition, machine learning systems are gradually revealing advantages of sparser and more modular architectures.
Compositional Attention: Disentangling Search and Retrieval
Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed.
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure.
Diffusion-Based Representation Learning
In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective and thus encodes the information needed for denoising.
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow.
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve.
