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Found 47 papers, 14 papers with code
Revisiting Rumour Stance Classification: Dealing with Imbalanced Data
Correctly classifying stances of replies can be significantly helpful for the automatic detection and classification of online rumours.
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification
The semi-supervised meta training approach avoids overfitting prototypical networks on a small number of labeled training examples and quickly learns cross-domain task-specific representation only from a few supporting examples.
SFMViT: SlowFast Meet ViT in Chaotic World
The task of spatiotemporal action localization in chaotic scenes is a challenging task toward advanced video understanding.
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
Skeleton-based action recognition has gained considerable traction thanks to its utilization of succinct and robust skeletal representations.
LLM-Enhanced Causal Discovery in Temporal Domain from Interventional Data
In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstream industrial tasks such as root cause analysis.
CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models
We present BenchmarkName, a novel benchmark to quantify LLM security risks and capabilities.
Event-assisted Low-Light Video Object Segmentation
In the realm of video object segmentation (VOS), the challenge of operating under low-light conditions persists, resulting in notably degraded image quality and compromised accuracy when comparing query and memory frames for similarity computation.
Bidirectional Generative Pre-training for Improving Time Series Representation Learning
Learning time-series representations for discriminative tasks has been a long-standing challenge.
A Neural-network Enhanced Video Coding Framework beyond ECM
In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies.
ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition Challenge
To promote speech processing and recognition research in driving scenarios, we build on the success of the Intelligent Cockpit Speech Recognition Challenge (ICSRC) held at ISCSLP 2022 and launch the ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) Challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records
This leads to a highly interpretable survival topic model that can infer PheCode-specific phenotype topics associated with patient mortality.
Joint Training or Not: An Exploration of Pre-trained Speech Models in Audio-Visual Speaker Diarization
The scarcity of labeled audio-visual datasets is a constraint for training superior audio-visual speaker diarization systems.
Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
We present a dense simultaneous localization and mapping (SLAM) method that uses 3D Gaussians as a scene representation.
Deeper into Self-Supervised Monocular Indoor Depth Estimation
One is the large areas of low-texture regions and the other is the complex ego-motion on indoor training datasets.
Extrapolatable Transformer Pre-training for Ultra Long Time-Series Forecasting
This underscores the limitations of the existing transformer-based architectures, particularly their scalability to handle large-scale data and ability to capture long-term temporal dependencies.
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks.
DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine Reading
The use of visually-rich documents (VRDs) in various fields has created a demand for Document AI models that can read and comprehend documents like humans, which requires the overcoming of technical, linguistic, and cognitive barriers.
Multiple Satellites Collaboration for Joint Code-aided CFOs and CPOs Estimation
Low Earth Orbit (LEO) satellites are being extensively researched in the development of secure Internet of Remote Things (IoRT).
Neural Metamaterial Networks for Nonlinear Material Design
Nonlinear metamaterials with tailored mechanical properties have applications in engineering, medicine, robotics, and beyond.
Designs and Implementations in Neural Network-based Video Coding
The past decade has witnessed the huge success of deep learning in well-known artificial intelligence applications such as face recognition, autonomous driving, and large language model like ChatGPT.
Point-SLAM: Dense Neural Point Cloud-based SLAM
We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input which anchors the features of a neural scene representation in a point cloud that is iteratively generated in an input-dependent data-driven manner.
Can We Identify Stance Without Target Arguments? A Study for Rumour Stance Classification
Considering a conversation thread, rumour stance classification aims to identify the opinion (e. g. agree or disagree) of replies towards a target (rumour story).
QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression
In this paper, we present a Quantization-error-aware Variable Rate Framework (QVRF) that utilizes a univariate quantization regulator a to achieve wide-range variable rates within a single model.
NLOST: Non-Line-of-Sight Imaging With Transformer
Specifically, after extracting the shallow features with the assistance of physics-based priors, we design two spatial-temporal self attention encoders to explore both local and global correlations within 3D NLOS data by splitting or downsampling the features into different scales, respectively.
MSV Challenge 2022: NPU-HC Speaker Verification System for Low-resource Indian Languages
Specifically, the weight transfer fine-tuning aims to constrain the distance of the weights between the pre-trained model and the fine-tuned model, which takes advantage of the previously acquired discriminative ability from the large-scale out-domain datasets and avoids catastrophic forgetting and overfitting at the same time.
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation
In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
Classifying COVID-19 vaccine narratives
This paper addresses the need for monitoring and analysing vaccine narratives online by introducing a novel vaccine narrative classification task, which categorises COVID-19 vaccine claims into one of seven categories.
Modeling electronic health record data using a knowledge-graph-embedded topic model
We evaluated its performance based on EHR reconstruction and drug imputation.
GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity
This paper describes the second-placed system on the leaderboard of SemEval-2022 Task 8: Multilingual News Article Similarity.
Minimum $\ell_{1}$-norm interpolators: Precise asymptotics and multiple descent
An evolving line of machine learning works observe empirical evidence that suggests interpolating estimators -- the ones that achieve zero training error -- may not necessarily be harmful.
Supervised multi-specialist topic model with applications on large-scale electronic health record data
Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine.
Transparent Checkpointing for OpenGL Applications on GPUs
This work presents transparent checkpointing of OpenGL applications, refining the split-process technique[1] for application in GPU-based 3D graphics.
Distributed, Parallel, and Cluster Computing D.4.5
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations
Recent advances in implicit neural representations show great promise when it comes to generating numerical solutions to partial differential equations.
Hierarchical Meta Reinforcement Learning for Multi-Task Environments
To address the problem brought by the environment, we propose a Meta Soft Hierarchical reinforcement learning framework (MeSH), in which each low-level sub-policy focuses on a specific sub-task respectively and high-level policy automatically learns to utilize low-level sub-policies through meta-gradients.
Hierarchical Reinforcement Learning
Meta Reinforcement Learning
+2
Spread-Transform Dither Modulation Watermarking of Deep Neural Network
DNN watermarking is receiving an increasing attention as a suitable mean to protect the Intellectual Property Rights associated to DNN models.
Randomized tests for high-dimensional regression: A more efficient and powerful solution
We investigate the problem of testing the global null in the high-dimensional regression models when the feature dimension $p$ grows proportionally to the number of observations $n$.
Deep Physics-aware Inference of Cloth Deformation for Monocular Human Performance Capture
Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera.
Nonvolatile electric control of exciton complex in monolayer MoSe$_2$ with two dimensional ferroelectric CuInP$_2$S$_6$
With the saturated ferroelectric polarization of CIPS, electron-doped or hole-doped MoSe$_2$ is realized in a single device with a large carrier density tunability up to $5\times 10^{12}$cm$^{-2}$.
Materials Science
Restrained Generative Adversarial Network against Overfitting in Numeric Data Augmentation
In recent studies, Generative Adversarial Network (GAN) is one of the popular schemes to augment the image dataset.
Lagrangian-Eulerian Multi-Density Topology Optimization with the Material Point Method
A quadrature-level connectivity graph-based method is adopted to avoid the artificial checkerboard issues commonly existing in multi-resolution topology optimization methods.
Computational Physics Computational Engineering, Finance, and Science Graphics
Study of Constrained Network Structures for WGANs on Numeric Data Generation
Especially, Isomorphic WGAN is the best in 15/20 experiments.
Deep Learning-Based Video Coding: A Review and A Case Study
For deep schemes, pixel probability modeling and auto-encoder are the two approaches, that can be viewed as predictive coding scheme and transform coding scheme, respectively.
Multimedia Image and Video Processing
Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images
As the rapid growth of high-speed and deep-tissue imaging in biomedical research, it is urgent to find a robust and effective denoising method to retain morphological features for further texture analysis and segmentation.
A latent topic model for mining heterogenous non-randomly missing electronic health records data
Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining.
Towards Coinductive Theory Exploration in Horn Clause Logic: Position Paper
Coinduction occurs in two guises in Horn clause logic: in proofs of self-referencing properties and relations, and in proofs involving construction of (possibly irregular) infinite data.
Convolutional Neural Network-Based Block Up-sampling for Intra Frame Coding
A block can be down-sampled before being compressed by normal intra coding, and then up-sampled to its original resolution.
Multimedia
Unsupervised Learning in Genome Informatics
Here we review several unsupervised learning methods for deciphering the genome-wide patterns of those DNA regions.
