Search Results for author:
Found 25 papers, 12 papers with code
Return of EM: Entity-driven Answer Set Expansion for QA Evaluation
Recently, directly using large language models (LLMs) has been shown to be the most reliable method to evaluate QA models.
BAMM: Bidirectional Autoregressive Motion Model
To address these challenges, we propose Bidirectional Autoregressive Motion Model (BAMM), a novel text-to-motion generation framework.
Can LLMs Recognize Toxicity? Structured Toxicity Investigation Framework and Semantic-Based Metric
In the pursuit of developing Large Language Models (LLMs) that adhere to societal standards, it is imperative to discern the existence of toxicity in the generated text.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
MMM: Generative Masked Motion Model
MMM consists of two key components: (1) a motion tokenizer that transforms 3D human motion into a sequence of discrete tokens in latent space, and (2) a conditional masked motion transformer that learns to predict randomly masked motion tokens, conditioned on the pre-computed text tokens.
Ranked #5 on
Motion Synthesis
on KIT Motion-Language
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
We first present CAMBIGNQ, a dataset consisting of 5, 654 ambiguous questions, each with relevant passages, possible answers, and a clarification question.
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
In this paper, we specifically target the gender bias issue of multilingual machine translation models for unambiguous cases where there is a single correct translation, and propose a bias mitigation method based on a novel approach.
A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators
From the drift and diffusion terms of the Fokker--Planck equation, unknown parameters of the system are identified.
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition
mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals.
Error-related Potential Variability: Exploring the Effects on Classification and Transferability
Brain-Computer Interfaces (BCI) have allowed for direct communication from the brain to external applications for the automatic detection of cognitive processes such as error recognition.
GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer
Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities.
Ranked #6 on
Multiview Gait Recognition
on CASIA-B
Privacy Enhancement for Cloud-Based Few-Shot Learning
Requiring less data for accurate models, few-shot learning has shown robustness and generality in many application domains.
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples.
Towards Interactive Reinforcement Learning with Intrinsic Feedback
Reinforcement learning (RL) and brain-computer interfaces (BCI) have experienced significant growth over the past decade.
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model.
Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
Two techniques which we focus on in this work are 1) node embeddings from random walk based methods and 2) knowledge graph embeddings.
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
Federated Learning (FL) over wireless multi-hop edge computing networks, i. e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm.
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
To solve such MDP, multi-agent reinforcement learning (MA-RL) algorithms along with domain-specific action space refining schemes are developed, which online learn the delay-minimum forwarding paths to minimize the model exchange latency between the edge devices (i. e., workers) and the remote server.
CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models
Specifically, we employ a two-stage augmentation pipeline to generate new claims and evidences from existing samples.
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
MutualNet is a general training methodology that can be applied to various network structures (e. g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e. g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets.
Demystifying Deep Neural Networks Through Interpretation: A Survey
Modern deep learning algorithms tend to optimize an objective metric, such as minimize a cross entropy loss on a training dataset, to be able to learn.
Few-Shot Keyword Spotting With Prototypical Networks
Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home.
Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data
In this study, we develop a novel graph-based framework for ADR signal detection using healthcare claims data.
Machine learning approach to remove ion interference effect in agricultural nutrient solutions
Once a readjustment model is established, application on ISE data can be done in real time.
Topological Data Analysis for Discourse Semantics?
In this paper we present new results on applying topological data analysis to discourse structures.
