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Found 41 papers, 19 papers with code
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue
This paper investigates the effectiveness of combining LLM and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods: CRS assisting LLM and LLM assisting CRS.
Provable Probabilistic Imaging using Score-Based Generative Priors
We also establish a theoretical analysis for characterizing the convergence of the PMC algorithms.
Sampling via Gradient Flows in the Space of Probability Measures
Our third contribution is to study, and develop efficient algorithms based on Gaussian approximations of the gradient flows; this leads to an alternative to particle methods.
Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution
Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for robust feature extraction from text images, and a Local Contour Awareness loss ($\mathcal{L}_{lca}$) to enhance the model's perception of details.
NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning
Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications.
A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
The study includes a set of experiments to support the theory and method, including approximating the GW distance, preserving the graph spectrum, classifying graphs using spectral information, and performing regression using graph convolutional networks.
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs
We introduce a priori Sobolev-space error estimates for the solution of nonlinear, and possibly parametric, PDEs using Gaussian process and kernel based methods.
U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation
In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios.
Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processes
We present a sparse Cholesky factorization algorithm for such kernel matrices based on the near-sparsity of the Cholesky factor under a new ordering of Diracs and derivative measurements.
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
The flow in the Gaussian space may be understood as a Gaussian approximation of the flow.
A Flexible Multi-view Multi-modal Imaging System for Outdoor Scenes
The experimental results show that multi-view point clouds greatly improve 3D object detection and tracking accuracy regardless of complex and various outdoor environments.
Robust convex biclustering with a tuning-free method
Therefore, we propose a tuning-free method for automatically selecting the optimal robustification parameter with high efficiency.
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning
Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning.
The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines
In the metric aspect, we design the new conversational DER (CDER) evaluation metric, which calculates the SD accuracy at the utterance level.
Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China
Housing quality is an essential proxy for regional wealth, security and health.
Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations
Randomly pivoted Cholesky (RPCholesky) is a natural algorithm for computing a rank-k approximation of an N x N positive semidefinite (psd) matrix.
Microwave Chirality Imaging for the Early Diagnosis of Neurological Degenerative Diseases
We propose a system to visualize the chirality of the protein in brains, which would be helpful to diagnose early neurological degenerative diseases in vivo.
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization
For online speaker diarization, samples arrive incrementally, and the overall distribution of the samples is invisible.
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
This paper revisits the approach from a matrix approximation perspective, and identifies two issues in the existing layer-wise sampling methods: suboptimal sampling probabilities and estimation biases induced by sampling without replacement.
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention
The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks.
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset
As a Mandarin speech dataset designed for dialog scenarios with high quality and rich annotations, MagicData-RAMC enriches the data diversity in the Mandarin speech community and allows extensive research on a series of speech-related tasks, including automatic speech recognition, speaker diarization, topic detection, keyword search, text-to-speech, etc.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Spatio-temporal Gait Feature with Global Distance Alignment
In this paper, we try to enhance the discrimination of spatio-temporal gait features from two aspects: effective extraction of spatio-temporal gait features and reasonable refinement of extracted features.
Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences
Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nyström Method
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
Revisiting Layer-wise Sampling in Fast Training for Graph Convolutional Networks
To accelerate the training of graph convolutional networks (GCN), many sampling-based methods have been developed for approximating the embedding aggregation.
A FAIR and AI-ready Higgs boson decay dataset
To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Region-Adaptive Deformable Network for Image Quality Assessment
Image quality assessment (IQA) aims to assess the perceptual quality of images.
Solving and Learning Nonlinear PDEs with Gaussian Processes
The main idea of our method is to approximate the solution of a given PDE as the maximum a posteriori (MAP) estimator of a Gaussian process conditioned on solving the PDE at a finite number of collocation points.
Fast Statistical Leverage Score Approximation in Kernel Ridge Regression
Nystr\"om approximation is a fast randomized method that rapidly solves kernel ridge regression (KRR) problems through sub-sampling the n-by-n empirical kernel matrix appearing in the objective function.
Accumulations of Projections--A Unified Framework for Random Sketches in Kernel Ridge Regression
Building a sketch of an n-by-n empirical kernel matrix is a common approach to accelerate the computation of many kernel methods.
A Corpus of Very Short Scientific Summaries
We present a new summarisation task, taking scientific articles and producing journal table-of-contents entries in the chemistry domain.
Pre-Trained Models for Heterogeneous Information Networks
We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network.
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation
The purpose of this paper is to study two paradigms of learning hierarchical parameters: one is from the probabilistic Bayesian perspective, in particular, the empirical Bayes approach that has been largely used in Bayesian statistics; the other is from the deterministic and approximation theoretic view, and in particular the kernel flow algorithm that was proposed recently in the machine learning literature.
Deep Attention Aware Feature Learning for Person Re-Identification
Visual attention has proven to be effective in improving the performance of person re-identification.
Ranked #8 on
Person Re-Identification
on Market-1501
Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation
A self-inverse network shares several distinct advantages: only one network instead of two, better generalization and more restricted parameter space.
Regularized Ensembles and Transferability in Adversarial Learning
Despite the considerable success of convolutional neural networks in a broad array of domains, recent research has shown these to be vulnerable to small adversarial perturbations, commonly known as adversarial examples.
Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers
If no sufficient decrease is found, the current point is called an approximate $R$-local minimizer.
Leveraging High-Dimensional Side Information for Top-N Recommendation
Top-$N$ recommender systems typically utilize side information to address the problem of data sparsity.
Information Retrieval
Content-Based Top-N Recommendation using Heterogeneous Relations
In this paper, we propose a content-based top-$N$ recommender system by learning the global term weights in profiles.
