Search Results for author:
Found 21 papers, 5 papers with code
Representation Learning for Sequential Volumetric Design Tasks
We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation.
Generalizable Pose Estimation Using Implicit Scene Representations
Most widely used methods learn to infer the object pose in a discriminative setup where the model filters useful information to infer the exact pose of the object.
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features.
SimCURL: Simple Contrastive User Representation Learning from Command Sequences
Our method introduces a user-session network architecture, as well as session dropout as a novel way of data augmentation.
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy.
COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions
Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e. g., multimodality, discontinuities, or deception.
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software.
Group-disentangled Representation Learning with Weakly-Supervised Regularization
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning.
Noise-robust classification with hypergraph neural network
This method is utilized to solve the noisy label learning problem.
Cauchy-Schwarz Regularized Autoencoder
To perform efficient inference for GMM priors, we introduce a new constrained objective based on the Cauchy-Schwarz divergence, which can be computed analytically for GMMs.
The Medical Scribe: Corpus Development and Model Performance Analyses
We used this annotation scheme to label a corpus of about 6k clinical encounters.
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.
How Good is the Bayes Posterior in Deep Neural Networks Really?
In this work we cast doubt on the current understanding of Bayes posteriors in popular deep neural networks: we demonstrate through careful MCMC sampling that the posterior predictive induced by the Bayes posterior yields systematically worse predictions compared to simpler methods including point estimates obtained from SGD.
Hydra: Preserving Ensemble Diversity for Model Distillation
As a result, the diversity of the ensemble predictions, stemming from each member, is lost.
On the Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximating posterior distributions in Bayesian neural networks.
To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods
To detect the irregular trade behaviors in the stock market is the important problem in machine learning field.
Learning to Infer Entities, Properties and their Relations from Clinical Conversations
Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e. g., symptoms) and their properties (e. g., duration).
Solve fraud detection problem by using graph based learning methods
In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem.
Extracting Symptoms and their Status from Clinical Conversations
This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status.
GAGAN: Geometry-Aware Generative Adversarial Networks
Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures.
The Generalized Mean Information Coefficient
Here we present the Generalized Mean Information Coefficient (GMIC), a generalization of MIC which incorporates a tuning parameter that can be used to modify the complexity of the association favored by the measure.
