Search Results for author:
Found 29 papers, 13 papers with code
Little Giants: Synthesizing High-Quality Embedding Data at Scale
In this paper, we introduce SPEED, a framework that aligns open-source small models (8B) to efficiently generate large-scale synthetic embedding data.
A Survey of Conversational Search
As a cornerstone of modern information access, search engines have become indispensable in everyday life.
Query-oriented Data Augmentation for Session Search
However, this paradigm neglects the symmetric nature of the relevance between the session context and document, i. e., the clicked documents can also be paired with different search contexts when training.
TieBot: Learning to Knot a Tie from Visual Demonstration through a Real-to-Sim-to-Real Approach
We introduce the Hierarchical Feature Matching approach to estimate a sequence of tie's meshes from the demonstration video.
Cooperative Advisory Residual Policies for Congestion Mitigation
Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers.
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
The main barrier is the lack of large-scale human-annotated dataset.
LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins
This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins.
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
ConvAug first generates multi-level augmented conversations to capture the diverse nature of conversational contexts.
Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training
Cloud solutions have gained significant popularity in the technology industry as they offer a combination of services and tools to tackle specific problems.
Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation
Besides, it infers session-level relevance labels based on implicit feedback.
End-to-End Retrieval with Learned Dense and Sparse Representations Using Lucene
The bi-encoder architecture provides a framework for understanding machine-learned retrieval models based on dense and sparse vector representations.
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image.
Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection
Previous methods use external knowledge as references for text generation to enhance factuality but often struggle with the knowledge mix-up(e. g., entity mismatch) of irrelevant references.
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment
We present ClothesNet: a large-scale dataset of 3D clothes objects with information-rich annotations.
Large Language Models for Information Retrieval: A Survey
This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity).
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
Explainability of Speech Recognition Transformers via Gradient-based Attention Visualization
In addition, we demonstrate the utilization of visualization result in three ways: (1) We visualize attention with respect to connectionist temporal classification (CTC) loss to train an ASR model with adversarial attention erasing regularization, which effectively decreases the word error rate (WER) of the model and improves its generalization capability.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search
Precisely understanding users' contextual search intent has been an important challenge for conversational search.
ISTVT: Interpretable Spatial-Temporal Video Transformer for Deepfake Detection
To address these two challenges, in this paper, we propose an Interpretable Spatial-Temporal Video Transformer (ISTVT), which consists of a novel decomposed spatial-temporal self-attention and a self-subtract mechanism to capture spatial artifacts and temporal inconsistency for robust Deepfake detection.
Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search
To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query.
From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner.
Causal Discovery from Sparse Time-Series Data Using Echo State Network
Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur.
Learning to Navigate Intersections with Unsupervised Driver Trait Inference
Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning.
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control
We explore the interpretation of sound for robot decision making, inspired by human speech comprehension.
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning
Our results show the feasibility of a robot learning commonsense knowledge automatically from web-based textual corpora, and the power of learned commonsense reasoning models in enabling a robot to autonomously perform tasks based on incomplete natural language instructions.
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
The increasing concern with misinformation has stimulated research efforts on automatic fact checking.
Cross Domain Knowledge Transfer for Person Re-identification
Building on the idea that identity classification, attribute recognition and re- identification share the same mid-level semantic representations, they can be trained sequentially by fine-tuning one based on another.
