Search Results for author:
Found 38 papers, 13 papers with code
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection
Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL).
NameGuess: Column Name Expansion for Tabular Data
Recent advances in large language models have revolutionized many sectors, including the database industry.
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
Recent advances in tabular data generation have greatly enhanced synthetic data quality.
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources
Our method combines the estimation of a dependency graph and conditional probabilities from the target location with the use of a Gaussian copula to leverage the available information from the auxiliary locations.
Training self-supervised peptide sequence models on artificially chopped proteins
In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins".
Ex-Ante Assessment of Discrimination in Dataset
Data owners face increasing liability for how the use of their data could harm under-priviliged communities.
SoFaiR: Single Shot Fair Representation Learning
To avoid discriminatory uses of their data, organizations can learn to map them into a representation that filters out information related to sensitive attributes.
Improving Zero-Shot Event Extraction via Sentence Simplification
We show how such context simplification can improve the performance of MRC-based event extraction by more than 5% for actor extraction and more than 10% for target extraction.
Causal Knowledge Guided Societal Event Forecasting
(ii) Given spatiotemporal non-independent and identically distributed (non-IID) data, modeling hidden confounders for accurate causal effect estimation is not trivial.
Asynchronous Federated Learning for Sensor Data with Concept Drift
We propose a novel approach, FedConD, to detect and deal with the concept drift on local devices and minimize the effect on the performance of models in asynchronous FL.
Cross-Lingual Text Classification of Transliterated Hindi and Malayalam
Transliteration is very common on social media, but transliterated text is not adequately handled by modern neural models for various NLP tasks.
Fair Representations by Compression
Organizations that collect and sell data face increasing scrutiny for the discriminatory use of data.
Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling
Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU).
Metric-Free Individual Fairness with Cooperative Contextual Bandits
Group fairness requires that different groups should be treated similarly which might be unfair to some individuals within a group.
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
By bridging the synchronous and asynchronous training through tiering, FedAT minimizes the straggler effect with improved convergence speed and test accuracy.
Learning Smooth and Fair Representations
Organizations that own data face increasing legal liability for its discriminatory use against protected demographic groups, extending to contractual transactions involving third parties access and use of the data.
Common-Knowledge Concept Recognition for SEVA
We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering.
Generative Multi-Stream Architecture For American Sign Language Recognition
With advancements in deep model architectures, tasks in computer vision can reach optimal convergence provided proper data preprocessing and model parameter initialization.
FineHand: Learning Hand Shapes for American Sign Language Recognition
American Sign Language recognition is a difficult gesture recognition problem, characterized by fast, highly articulate gestures.
Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Tweets for Emergency Services
As deep networks struggle with sparse datasets, we show that this can be improved by sharing a base layer for multi-task learning and domain adversarial training.
Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift
At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data.
Academic Performance Estimation with Attention-based Graph Convolutional Networks
Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses; and how students acquire knowledge across them.
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers.
Low Rank Factorization for Compact Multi-Head Self-Attention
Effective representation learning from text has been an active area of research in the fields of NLP and text mining.
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data
In this paper, we present an Asynchronous Online Federated Learning (ASO-Fed) framework, where the edge devices perform online learning with continuous streaming local data and a central server aggregates model parameters from clients.
Sign Language Recognition Analysis using Multimodal Data
Despite having similarity with the well-studied human activity recognition, the use of 3D skeleton data in sign language recognition is rare.
Federated Multi-task Hierarchical Attention Model for Sensor Analytics
The attention mechanism of the proposed model seeks to extract feature representations from the input and learn a shared representation focused on time dimensions across multiple sensors.
Multi-Differential Fairness Auditor for Black Box Classifiers
Machine learning algorithms are increasingly involved in sensitive decision-making process with adversarial implications on individuals.
Reliable Deep Grade Prediction with Uncertainty Estimation
Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models.
ALE: Additive Latent Effect Models for Grade Prediction
Grade prediction methods seek to estimate a grade that a student may achieve in a course that she may take in the future (e. g., next term).
Grade Prediction with Temporal Course-wise Influence
The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space.
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning
Multi-task learning (MTL) is a supervised learning paradigm in which the prediction models for several related tasks are learned jointly to achieve better generalization performance.
Embedding Feature Selection for Large-scale Hierarchical Classification
Our experimental evaluation on text and image datasets with varying distribution of features, classes and instances shows upto 3x order of speed-up on massive datasets and upto 45% less memory requirements for storing the weight vectors of learned model without any significant loss (improvement for some datasets) in the classification accuracy.
Inconsistent Node Flattening for Improving Top-down Hierarchical Classification
In this paper, we propose two different data-driven approaches (local and global) for hierarchical structure modification that identifies and flattens inconsistent nodes present within the hierarchy.
Predicting Performance on MOOC Assessments using Multi-Regression Models
The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs).
Filter based Taxonomy Modification for Improving Hierarchical Classification
Experimental comparisons of top-down HC with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i:e:, defined by expert), clustered hierarchy and flattening based hierarchy modification approaches.
