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Found 33 papers, 11 papers with code
MatterGen: a generative model for inorganic materials design
We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset.
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
Markov processes are widely used mathematical models for describing dynamic systems in various fields.
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$).
DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution
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, and hybrid combinations thereof.
An Information-theoretic Approach to Distribution Shifts
Safely deploying machine learning models to the real world is often a challenging process.
Regularized Policies are Reward Robust
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 overfitting to a local optimal policy.
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
We analyze the influence of adversarial training on the loss landscape of machine learning models.
Conservative Uncertainty Estimation By Fitting Prior Networks
Obtaining high-quality uncertainty estimates is essential for many applications of deep neural networks.
On Certifying Non-uniform Bound against Adversarial Attacks
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.
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
We therefore endow VAEs with a Poincar\'e ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space.
Depth and nonlinearity induce implicit exploration for RL
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).
The Mutual Autoencoder: Controlling Information in Latent Code Representations
Thus, we decouple the choice of decoder capacity and the latent code dimensionality from the amount of information stored in the code.
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently even for small minibatch sizes, resulting in significantly shorter overall training times.
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control.
Geometry of Optimization and Implicit Regularization in Deep Learning
We argue that the optimization plays a crucial role in generalization of deep learning models through implicit regularization.
Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain.
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
We propose the Gaussian attention model for content-based neural memory access.
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
In this paper, we propose Quantized SGD (QSGD), a family of compression schemes which allow the compression of gradient updates at each node, while guaranteeing convergence under standard assumptions.
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights.
Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA
In this paper, we clarify the behavior of VB learning in probabilistic PCA (or fully-observed matrix factorization).
Data-Dependent Path Normalization in Neural Networks
We propose a unified framework for neural net normalization, regularization and optimization, which includes Path-SGD and Batch-Normalization and interpolates between them across two different dimensions.
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification
We theoretically and experimentally investigate tensor-based regression and classification.
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
We consider the problem of recovering a low-rank tensor from its noisy observation.
Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
Similarity between objects is multi-faceted and it can be easier for human annotators to measure it when the focus is on a specific aspect.
Norm-Based Capacity Control in Neural Networks
We investigate the capacity, convexity and characterization of a general family of norm-constrained feed-forward networks.
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks.
Multitask learning meets tensor factorization: task imputation via convex optimization
We study a multitask learning problem in which each task is parametrized by a weight vector and indexed by a pair of indices, which can be e. g, (consumer, time).
Spectral norm of random tensors
We show that the spectral norm of a random $n_1\times n_2\times \cdots \times n_K$ tensor (or higher-order array) scales as $O\left(\sqrt{(\sum_{k=1}^{K}n_k)\log(K)}\right)$ under some sub-Gaussian assumption on the entries.
Convex Tensor Decomposition via Structured Schatten Norm Regularization
We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decomposition, and connect tensor decomposition with wider literature on structured sparsity.
Perfect Dimensionality Recovery by Variational Bayesian PCA
The variational Bayesian (VB) approach is one of the best tractable approximations to the Bayesian estimation, and it was demonstrated to perform well in many applications.
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
We present a novel algebraic combinatorial view on low-rank matrix completion based on studying relations between a few entries with tools from algebraic geometry and matroid theory.
Statistical Performance of Convex Tensor Decomposition
We analyze the statistical performance of a recently proposed convex tensor decomposition algorithm.
Global Analytic Solution for Variational Bayesian Matrix Factorization
Bayesian methods of matrix factorization (MF) have been actively explored recently as promising alternatives to classical singular value decomposition.
