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Found 63 papers, 15 papers with code
Understanding the Tradeoff between Cost and Quality of Expert Annotations for Keyphrase Extraction
We evaluate these annotation strategies with respect to their cost and on the task of learning keyphrase extraction models applied with an experimental dataset in the real-estate domain.
Pointsoup: High-Performance and Extremely Low-Decoding-Latency Learned Geometry Codec for Large-Scale Point Cloud Scenes
Despite considerable progress being achieved in point cloud geometry compression, there still remains a challenge in effectively compressing large-scale scenes with sparse surfaces.
Training-free Boost for Open-Vocabulary Object Detection with Confidence Aggregation
Specifically, in the region-proposal stage, proposals that contain novel instances showcase lower objectness scores, since they are treated as background proposals during the training phase.
Binomial Self-compensation for Motion Error in Dynamic 3D Scanning
Phase shifting profilometry (PSP) is favored in high-precision 3D scanning due to its high accuracy, robustness, and pixel-wise property.
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs
To our best knowledge, we are the first work that achieves almost lossless quantization performance for LLMs under a data-independent setting and our algorithm runs over 10 times faster than the data-dependent methods.
HoloVIC: Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative
Based on HoloVIC, we formulated four tasks to facilitate the development of related research.
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs.
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
Most existing OOD detection methods focused on exploring advanced training skills or training-free tricks to prevent the model from yielding overconfident confidence score for unknown samples.
Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance
Query-LIFE utilizes a query-based multimodal fusion to effectively incorporate the image and title based on the product types.
Phase Guided Light Field for Spatial-Depth High Resolution 3D Imaging
On 3D imaging, light field cameras typically are of single shot, and however, they heavily suffer from low spatial resolution and depth accuracy.
A Spatial-Temporal Transformer based Framework For Human Pose Assessment And Correction in Education Scenarios
Human pose assessment and correction play a crucial role in applications across various fields, including computer vision, robotics, sports analysis, healthcare, and entertainment.
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity
Traditional pruning methods are known to be challenging to work in Large Language Models (LLMs) for Generative AI because of their unaffordable training process and large computational demands.
GraphText: Graph Reasoning in Text Space
Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language.
On Regularized Sparse Logistic Regression
Sparse logistic regression is for classification and feature selection simultaneously.
Uncertainty-aware Unsupervised Multi-Object Tracking
The resulting accurate pseudo-tracklets boost learning the feature consistency.
Strictly Low Rank Constraint Optimization -- An Asymptotically $\mathcal{O}(\frac{1}{t^2})$ Method
We study a class of non-convex and non-smooth problems with \textit{rank} regularization to promote sparsity in optimal solution.
Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis
The increasing scale of alternating current and direct current (AC/DC) hybrid systems necessitates a faster power flow analysis tool than ever.
SDFReg: Learning Signed Distance Functions for Point Cloud Registration
We then propose to alternately optimize the implicit function and the registration between the implicit function and point cloud.
Pyramid Multi-branch Fusion DCNN with Multi-Head Self-Attention for Mandarin Speech Recognition
On the other hand, we need to reduce the dimension of each subspace to keep the size of the overall feature space unchanged when we increase the number of heads, which will significantly weaken the ability to represent the feature of each subspace.
X-SepFormer: End-to-end Speaker Extraction Network with Explicit Optimization on Speaker Confusion
To mitigate the imperative SC issue, we reformulate the training objective and propose two novel loss schemes that explore the metric of reconstruction improvement performance defined at small chunk-level and leverage the metric associated distribution information.
Adaptive Weighted Multiview Kernel Matrix Factorization with its application in Alzheimer's Disease Analysis -- A clustering Perspective
Recent technology and equipment advancements provide with us opportunities to better analyze Alzheimer's disease (AD), where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance.
A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization
The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks.
Improving Target Speaker Extraction with Sparse LDA-transformed Speaker Embeddings
As a practical alternative of speech separation, target speaker extraction (TSE) aims to extract the speech from the desired speaker using additional speaker cue extracted from the speaker.
Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers
Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem.
Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction
The integration of multi-modal data, such as pathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions.
Multi-Task Learning with Prior Information
Multi-task learning aims to boost the generalization performance of multiple related tasks simultaneously by leveraging information contained in those tasks.
KAST: Knowledge Aware Adaptive Session Multi-Topic Network for Click-Through Rate Prediction
Capturing the evolving trends of user interest is important for both recommendation systems and advertising systems, and user behavior sequences have been successfully used in Click-Through-Rate(CTR) prediction problems.
A GPU-accelerated Algorithm for Distinct Discriminant Canonical Correlation Network
In this paper, a GPU-based accelerated algorithm is proposed to further optimize the DDCCANet algorithm.
Joint Speech Activity and Overlap Detection with Multi-Exit Architecture
Overlapped speech detection (OSD) is critical for speech applications in scenario of multi-party conversion.
Speech Enhancement with Perceptually-motivated Optimization and Dual Transformations
To validate the effectiveness of our proposed model, extensive experiments are conducted on the DNS2020 dataset.
Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective
Nonnegative matrix factorization (NMF) is widely used for clustering with strong interpretability.
Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning
In this paper, we first demonstrate the flaw of Weighted QMIX using an ordinary One-Step Matrix Game (OMG), that no matter how the weight is chosen, Weighted QMIX struggles to deal with non-monotonic value decomposition problems with a large variance of reward distributions.
Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling
First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection.
Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network
Specifically, CClusnet-Inseg uses each pixel to predict object centers and trace these centers to form masks based on clustering results, which consists of a network for segmentation and center offset vector map, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, Centers-to-Mask (C2M), and Remain-Centers-to-Mask (RC2M) algorithms.
Enriched Robust Multi-View Kernel Subspace Clustering
To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering.
MKQ-BERT: Quantized BERT with 4-bits Weights and Activations
In this work, we propose MKQ-BERT, which further improves the compression level and uses 4-bits for quantization.
Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing
A convolutional, long short-term memory model combined with a hexagonal convolution operation (H-ConvLSTM) is proposed to explore the complex spatial and temporal relations.
Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting
However, the transformer directly partitions the crowd images into a series of tokens, which may not be a good choice due to each pedestrian being an independent individual, and the parameter number of the network is very large.
Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention
To reduce the quadratic computation complexity caused by each query attending to all keys/values, various methods have constrained the range of attention within local regions, where each query only attends to keys/values within a hand-crafted window.
Spherical Matrix Factorization
However, most of the studies aim to minimize the loss by measuring the Euclidean distance, though in some fields, angle distance is known to be more important and critical for analysis.
Robust Principal Component Analysis: A Construction Error Minimization Perspective
In this paper we propose a novel optimization framework to systematically solve robust PCA problem with rigorous theoretical guarantee, based on which we investigate very computationally economic updating algorithms.
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments
Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning.
Exact Sparse Orthogonal Dictionary Learning
Over the past decade, learning a dictionary from input images for sparse modeling has been one of the topics which receive most research attention in image processing and compressed sensing.
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating
In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering
In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers.
Ranked #4 on
Passage Retrieval
on Natural Questions
Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech
In this paper, we propose multi-band MelGAN, a much faster waveform generation model targeting to high-quality text-to-speech.
Sound Audio and Speech Processing
Incentivized Exploration for Multi-Armed Bandits under Reward Drift
We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide biased feedback on reward.
Gated Multiple Feedback Network for Image Super-Resolution
The rapid development of deep learning (DL) has driven single image super-resolution (SR) into a new era.
Ranked #18 on
Image Super-Resolution
on BSD100 - 4x upscaling
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension
In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC.
Spherical Principal Component Analysis
However, most of the studies on PCA aim to minimize the loss after projection, which usually measures the Euclidean distance, though in some fields, angle distance is known to be more important and critical for analysis.
Shubnikov-de Haas and de Haas-van Alphen oscillations in topological semimetal CaAl4
An electron-type quasi-2D Fermi surface is found by the angle-dependent Shubnikov-de Haas oscillations, de Haas-van Alphen oscillations and the first-principles calculations.
Materials Science Mesoscale and Nanoscale Physics
Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization
Symmetric nonnegative matrix factorization (NMF), a special but important class of the general NMF, is demonstrated to be useful for data analysis and in particular for various clustering tasks.
Deep Item-based Collaborative Filtering for Top-N Recommendation
In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items.
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task
Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between the candidate and reference answers, such as ROUGE and BLEU.
Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l1-Norm Distances
To tackle these two challenges, in this paper we propose a novel image representation learning method that can integrate the local patches (the instances) of an input image (the bag) and its holistic representation into one single-vector representation.
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine.
Ranked #3 on
Question Answering
on MS MARCO
Spatial Image Steganography Based on Generative Adversarial Network
The architecture contain three component modules: a generator, an embedding simulator and a discriminator.
Multimedia
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators
In the case of phase measuring profilometry (PMP), the projected patterns are composed of a rolling sinusoidal wave, but as a set of time-multiplexed patterns, PMP requires the target surface to remain motionless or for scanning to be performed at such high rates that any movement is small.
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition
Therefore, an action can be represented as a subspace corresponding to a point on a Grassmann manifold.
