Search Results for author:
Found 29 papers, 9 papers with code
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base.
V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions
Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection.
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
In particular, local editing methods, which directly update model parameters, are more suitable for updating a small amount of knowledge.
Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM
To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness.
How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model
The study surveys existing modal alignment methods in MLLMs into four groups: (1) Multimodal Converters that change data into something LLMs can understand; (2) Multimodal Perceivers to improve how LLMs perceive different types of data; (3) Tools Assistance for changing data into one common format, usually text; and (4) Data-Driven methods that teach LLMs to understand specific types of data in a dataset.
MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations.
A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction
PERL contains a physics model and a residual learning model.
P-ROCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective
In recent years, two competitive time series classification models, namely, ROCKET and MINIROCKET, have garnered considerable attention due to their low training cost and high accuracy.
HAMUR: Hyper Adapter for Multi-Domain Recommendation
Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains.
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation
To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly.
Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks
Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix.
PMET: Precise Model Editing in a Transformer
To achieve more precise model editing, we analyze hidden states of MHSA and FFN, finding that MHSA encodes certain general knowledge extraction patterns.
Exploring Continual Learning for Code Generation Models
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance.
A Static Evaluation of Code Completion by Large Language Models
Large language models trained on code have shown great potential to increase productivity of software developers.
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Such large models incur significant resource usage (in terms of memory, latency, and dollars) as well as carbon footprint.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
ContraCLM: Contrastive Learning For Causal Language Model
Specifically, we attain $44\%$ relative improvement on the Semantic Textual Similarity tasks and $34\%$ on Code-to-Code Search tasks.
Hard instance learning for quantum adiabatic prime factorization
Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography.
Uncertainty Set Prediction of Aggregated Wind Power Generation based on Bayesian LSTM and Spatio-Temporal Analysis
This paper focuses on the uncertainty set prediction of the aggregated generation of geographically distributed wind farms.
Quantum Adiabatic Doping for Atomic Fermi-Hubbard Quantum Simulations
Its feasibility has been demonstrated with numerical simulations of the adiabatic preparation for certain incommensurate particle-doping fractions, where the major problem to circumvent is the atomic localization in the incommensurate lattice.
Quantum Gases Strongly Correlated Electrons Quantum Physics
Not All Attention Is Needed: Gated Attention Network for Sequence Data
Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states.
Learning to Abstract for Memory-augmented Conversational Response Generation
In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation.
Quantum Adiabatic Algorithm Design using Reinforcement Learning
We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-algorithm obtained by our RL approach leads to significant improvement in the resultant success probability.
Review Helpfulness Assessment based on Convolutional Neural Network
In this paper we describe the implementation of a convolutional neural network (CNN) used to assess online review helpfulness.
Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs
The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection.
Learning Sparse Deep Feedforward Networks via Tree Skeleton Expansion
An important characteristic of FNN structures learned this way is that they are sparse.
Building Sparse Deep Feedforward Networks using Tree Receptive Fields
We use Chow-Liu's algorithm to learn a tree-structured probabilistic model for the units at the current level, use the tree to identify subsets of units that are strongly correlated, and introduce a new unit with receptive field over the subsets.
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features.
Learning Parsimonious Deep Feed-forward Networks
Convolutional neural networks and recurrent neural networks are designed with network structures well suited to the nature of spacial and sequential data respectively.
