Search Results for author:
Found 28 papers, 8 papers with code
Data Cleaning Tools for Token Classification Tasks
We incorporated our sieve into an end-to-end system for cleaning NLP corpora, implemented as a modular collection of Jupyter notebooks built on extensions to the Pandas DataFrame library.
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning
Discovering Out-of-Domain(OOD) intents is essential for developing new skills in a task-oriented dialogue system.
Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots
Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit.
VLPose: Bridging the Domain Gap in Pose Estimation with Language-Vision Tuning
Our research aims to bridge the domain gap between natural and artificial scenarios with efficient tuning strategies.
Particle-Based Shape Modeling for Arbitrary Regions-of-Interest
We propose an extension to \particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest.
Adaptive Gating in Mixture-of-Experts based Language Models
Existing MoE model adopts a fixed gating network where each token is computed by the same number of experts.
Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions
This RBF-based shape representation offers a rich self-supervised signal for the network to estimate a continuous, yet compact representation of the underlying surface that can adapt to complex geometries in a data-driven manner.
Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge
3D object detection plays a pivotal role in many applications, most notably autonomous driving and robotics.
Aryl: An Elastic Cluster Scheduler for Deep Learning
We introduce Aryl, a new cluster scheduler to address these problems.
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature.
Ranked #1 on
Sentence Embeddings For Biomedical Texts
on BIOSSES
Adversarial Self-Supervised Learning for Out-of-Domain Detection
Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system.
More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances.
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System
Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set.
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system.
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space
Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system.
Identifying Incorrect Labels in the CoNLL-2003 Corpus
The CoNLL-2003 corpus for English-language named entity recognition (NER) is one of the most influential corpora for NER model research.
Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
The imaging process required for 3D reconstructions involves a highly iterative and empirical screening process, starting with the acquisition of low magnification images of the cryo-EM grids.
Simulating Performance of ML Systems with Offline Profiling
We advocate that simulation based on offline profiling is a promising approach to better understand and improve the complex ML systems.
Greedy Sampling for Approximate Clustering in the Presence of Outliers
One the one hand, they possess good theoretical approximation guarantees, and on the other, they are fast and easy to implement.
MANELA: A Multi-Agent Algorithm for Learning Network Embeddings
On the other hand, learning network embeddings on distributively stored networks still remained understudied: To the best of our knowledge, all existing algorithms for learning network embeddings have hitherto been exclusively centralized and thus cannot be applied to these networks.
Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment
A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL).
Saec: Similarity-Aware Embedding Compression in Recommendation Systems
Production recommendation systems rely on embedding methods to represent various features.
Flash: Efficient Dynamic Routing for Offchain Networks
Mice payments are directly sent by looking up a routing table with a few precomputed paths to reduce probing overhead.
Networking and Internet Architecture
Stanza: Layer Separation for Distributed Training in Deep Learning
Thus, we propose layer separation in distributed training: the majority of the nodes just train the convolutional layers, and the rest train the fully connected layers only.
Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks
To the best of our knowledge, this is the first time that such systematic presence of analogies is observed in network and document embeddings.
Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems
In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account.
Measuring Territorial Control in Civil Wars Using Hidden Markov Models: A Data Informatics-Based Approach
Territorial control is a key aspect shaping the dynamics of civil war.
Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research.
