research
∙
08/25/2023
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series
Real-world time series data that commonly reflect sequential human behav...
share
research
∙
08/01/2023
Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models
Today, large language models (LLMs) are taught to use new tools by provi...
research
∙
05/26/2023
LANISTR: Multimodal Learning from Structured and Unstructured Data
Multimodal large-scale pretraining has shown impressive performance gain...
research
∙
05/03/2023
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Deploying large language models (LLMs) is challenging because they are m...
research
∙
04/07/2023
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Selective prediction aims to learn a reliable model that abstains from m...
research
∙
03/10/2023
TSMixer: An all-MLP Architecture for Time Series Forecasting
Real-world time-series datasets are often multivariate with complex dyna...
research
∙
02/06/2023
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
In Composed Image Retrieval (CIR), a user combines a query image with te...
research
∙
11/30/2022
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Semi-supervised anomaly detection is a common problem, as often the data...
research
∙
06/15/2022
Test-Time Adaptation for Visual Document Understanding
Self-supervised pretraining has been able to produce transferable repres...
research
∙
06/13/2022
Invariant Structure Learning for Better Generalization and Causal Explainability
Learning the causal structure behind data is invaluable for improving ge...
research
∙
06/05/2022
Interpretable Mixture of Experts for Structured Data
With the growth of machine learning for structured data, the need for re...
research
∙
06/02/2022
Prefix Conditioning Unifies Language and Label Supervision
Vision-language contrastive learning suggests a new learning paradigm by...
research
∙
03/30/2022
Learning Instance-Specific Adaptation for Cross-Domain Segmentation
We propose a test-time adaptation method for cross-domain image segmenta...
research
∙
03/16/2022
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction
Sequence modeling has demonstrated state-of-the-art performance on natur...
research
∙
03/03/2022
Data-Efficient and Interpretable Tabular Anomaly Detection
Anomaly detection (AD) plays an important role in numerous applications....
research
∙
02/04/2022
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series
Real-world time-series datasets often violate the assumptions of standar...
research
∙
02/04/2022
Decoupling Local and Global Representations of Time Series
Real-world time series data are often generated from several sources of ...
research
∙
01/10/2022
Towards Group Robustness in the presence of Partial Group Labels
Learning invariant representations is an important requirement when trai...
research
∙
12/21/2021
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
We introduce anomaly clustering, whose goal is to group data into semant...
research
∙
09/07/2021
Learning Fast Sample Re-weighting Without Reward Data
Training sample re-weighting is an effective approach for tackling data ...
research
∙
06/21/2021
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Natural reading orders of words are crucial for information extraction f...
research
∙
06/14/2021
Controlling Neural Networks with Rule Representations
We propose a novel training method to integrate rules into deep learning...
research
∙
06/11/2021
Self-Trained One-class Classification for Unsupervised Anomaly Detection
Anomaly detection (AD), separating anomalies from normal data, has vario...
research
∙
05/26/2021
Aggregating Nested Transformers
Although hierarchical structures are popular in recent vision transforme...
research
∙
04/08/2021
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
We aim at constructing a high performance model for defect detection tha...
research
∙
01/11/2021
Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts
Learning visual knowledge from massive weakly-labeled web videos has att...
research
∙
11/04/2020
Learning and Evaluating Representations for Deep One-class Classification
We present a two-stage framework for deep one-class classification. We f...
research
∙
10/19/2020
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Recent advances in semi-supervised learning (SSL) demonstrate that a com...
research
∙
08/03/2020
Interpretable Sequence Learning for COVID-19 Forecasting
We propose a novel approach that integrates machine learning into compar...
research
∙
05/10/2020
A Simple Semi-Supervised Learning Framework for Object Detection
Semi-supervised learning (SSL) has promising potential for improving the...
research
∙
02/16/2020
Differentiable Top-k Operator with Optimal Transport
The top-k operation, i.e., finding the k largest or smallest elements fr...
research
∙
12/19/2019
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
Multi-horizon forecasting problems often contain a complex mix of inputs...
research
∙
12/03/2019
Distance-Based Learning from Errors for Confidence Calibration
Deep neural networks (DNNs) are poorly-calibrated when trained in conven...
research
∙
10/17/2019
On Concept-Based Explanations in Deep Neural Networks
Deep neural networks (DNNs) build high-level intelligence on low-level r...
research
∙
10/16/2019
Generative Modeling for Small-Data Object Detection
This paper explores object detection in the small data regime, where onl...
research
∙
10/16/2019
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost
Active learning (AL) integrates data labeling and model training to mini...
research
∙
10/01/2019
IEG: Robust Neural Network Training to Tackle Severe Label Noise
Collecting large-scale data with clean labels for supervised training of...
research
∙
09/26/2019
RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling
Understanding black-box machine learning models is important towards the...
research
∙
09/25/2019
Data Valuation using Reinforcement Learning
Quantifying the value of data is a fundamental problem in machine learni...
research
∙
09/20/2019
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels
Recently deep neural networks have shown their capacity to memorize trai...
research
∙
08/29/2019
Learning to Transfer Learn
We propose a novel framework, learning to transfer learn (L2TL), to impr...
research
∙
08/20/2019
TabNet: Attentive Interpretable Tabular Learning
We propose a novel high-performance interpretable deep tabular data lear...
research
∙
03/15/2019
Inserting Videos into Videos
In this paper, we introduce a new problem of manipulating a given video ...
research
∙
02/26/2019
Harmonic Unpaired Image-to-image Translation
The recent direction of unpaired image-to-image translation is on one ha...
research
∙
02/17/2019
Attention-Based Prototypical Learning Towards Interpretable, Confident and Robust Deep Neural Networks
We propose a new framework for prototypical learning that bases decision...
research
∙
12/22/2016
Learning from Simulated and Unsupervised Images through Adversarial Training
With recent progress in graphics, it has become more tractable to train ...
research
∙
11/20/2015
Personalizing Human Video Pose Estimation
We propose a personalized ConvNet pose estimator that automatically adap...
research
∙
06/09/2015