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Found 16 papers, 5 papers with code
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics.
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems
Learning dynamics from dissipative chaotic systems is notoriously difficult due to their inherent instability, as formalized by their positive Lyapunov exponents, which exponentially amplify errors in the learned dynamics.
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning.
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed.
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians.
Learning with Stochastic Orders
Learning high-dimensional distributions is often done with explicit likelihood modeling or implicit modeling via minimizing integral probability metrics (IPMs).
Semi-Parametric Inducing Point Networks and Neural Processes
We introduce semi-parametric inducing point networks (SPIN), a general-purpose architecture that can query the training set at inference time in a compute-efficient manner.
Predicting Deep Neural Network Generalization with Perturbation Response Curves
However, despite these successes, the recent Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition suggests that there is a need for more robust and efficient measures of network generalization.
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations
Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in drug discovery and material design.
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
Gradient flows are a powerful tool for optimizing functionals in general metric spaces, including the space of probabilities endowed with the Wasserstein metric.
Gi and Pal Scores: Deep Neural Network Generalization Statistics
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks.
Alleviating Noisy Data in Image Captioning with Cooperative Distillation
Image captioning systems have made substantial progress, largely due to the availability of curated datasets like Microsoft COCO or Vizwiz that have accurate descriptions of their corresponding images.
Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO.
Tabular Transformers for Modeling Multivariate Time Series
This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.
Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics
In this work, we propose a method for measuring how well the latent space of deep generative models is able to encode structural and chemical features of molecular datasets by correlating latent space metrics with metrics from the field of topological data analysis (TDA).
