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Found 185 papers, 64 papers with code
Are Language-Agnostic Sentence Representations Actually Language-Agnostic?
With the emergence of pre-trained multilingual models, multilingual embeddings have been widely applied in various natural language processing tasks.
基于情感增强非参数模型的社交媒体观点聚类(A Sentiment Enhanced Nonparametric Model for Social Media Opinion Clustering)
“本文旨在使用文本聚类技术, 将社交媒体文本根据用户主张的观点汇总, 直观呈现网民群体所持有的不同立场。针对社交媒体文本模式复杂与情感丰富等特点, 本文提出使用情感分布增强方法改进现有的非参数短文本聚类算法, 以高斯分布建模文本情感, 捕获文本情感特征的同时能够自动确定聚类簇数量并实现观点聚类。在公开数据集上的实验显示, 该方法在多项聚类指标上取得了超越现有模型的聚类表现, 并在主观性较强的数据集中具有更显著的优势。”
Distributionally Robust Control Synthesis for Stochastic Systems with Safety and Reach-Avoid Specifications
We investigate the problem of synthesizing distributionally robust control policies for stochastic systems under safety and reach-avoid specifications.
OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios
We introduce ShareChatX, the first comprehensive, large-scale dataset for spoken dialogue that spans diverse scenarios.
Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
Existing full-reference image quality assessment (FR-IQA) methods often fail to capture the complex causal mechanisms that underlie human perceptual responses to image distortions, limiting their ability to generalize across diverse scenarios.
A Pioneering Neural Network Method for Efficient and Robust Fluid Simulation
Compared to traditional SPH methods, our speed improved approximately 10 times.
Language model driven: a PROTAC generation pipeline with dual constraints of structure and property
Finally, a two-round screening of the preliminary generated molecules using a multidimensional property prediction model to generate a batch of PROTAC molecules capable of degrading disease-relevant target proteins for validation in vitro experiments, thus achieving a complete solution for AI-assisted PROTAC drug generation.
ZeroGS: Training 3D Gaussian Splatting from Unposed Images
While previous methods can reconstruct from a few unposed images, they are not applicable when images are unordered or densely captured.
Mastering Chinese Chess AI (Xiangqi) Without Search
We have developed a high-performance Chinese Chess AI that operates without reliance on search algorithms.
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs
To our knowledge, uniINF is the first parameter-free algorithm to achieve the BoBW property for the heavy-tailed MAB problem.
Recurrent Interpolants for Probabilistic Time Series Prediction
Sequential models like recurrent neural networks and transformers have become standard for probabilistic multivariate time series forecasting across various domains.
Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation
Based on these discrete codes, we enhance the collaborative information of contrastive views by considering neighborhood structure and semantic relevance respectively.
Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction
This method enhances the regional explainability of machine learning models and offers wider applicability compared to existing methods.
GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation
GeoLRM tackles these issues by incorporating a novel 3D-aware transformer structure that directly processes 3D points and uses deformable cross-attention mechanisms to effectively integrate image features into 3D representations.
Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property
While demonstrated herein with rupture properties, this learning approach is transferable to solve similar problems of data scarcity across the nuclear industry.
DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus
With its superior rendering performance and high-fidelity rendering quality, 3DGS is excelling at its previous NeRF counterparts.
ViViD: Video Virtual Try-on using Diffusion Models
Video virtual try-on aims to transfer a clothing item onto the video of a target person.
Minimum-Variance Recursive State Estimation for 2-D Systems: When Asynchronous Multi-Channel Delays meet Energy Harvesting Constraints
Based on the reconstructed activated observation sequence and activated probability, a novel unbiased h+1-step recursive estimator is constructed.
Variational Schrödinger Diffusion Models
To improve the scalability while preserving efficient transportation plans, we leverage variational inference to linearize the forward score functions (variational scores) of SB and restore simulation-free properties in training backward scores.
Ranked #10 on
Image Generation
on CIFAR-10
HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis
However, due to the complexity of medical images and similar characteristics of different tissue cells, existing methods face great challenges in meeting their biological consistency.
Representation Learning of Daily Movement Data Using Text Encoders
Time-series representation learning is a key area of research for remote healthcare monitoring applications.
Optimal Control Synthesis of Markov Decision Processes for Efficiency with Surveillance Tasks
Our objective is to synthesize a control policy that ensures the surveillance task while maximizes the efficiency.
FEEL: A Framework for Evaluating Emotional Support Capability with Large Language Models
Emotional Support Conversation (ESC) is a typical dialogue that can effectively assist the user in mitigating emotional pressures.
BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection
Humans can easily distinguish the known and unknown categories and can recognize the unknown object by learning it once instead of repeating it many times without forgetting the learned object.
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
In this paper, we introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures (LRM) and general function approximation.
Distributional Reinforcement Learning
reinforcement-learning
+2
Provably Efficient Partially Observable Risk-Sensitive Reinforcement Learning with Hindsight Observation
This work pioneers regret analysis of risk-sensitive reinforcement learning in partially observable environments with hindsight observation, addressing a gap in theoretical exploration.
Floorplanning of VLSI by Mixed-Variable Optimization
FA-GSS uses the Golden Section strategy to optimize both wirelength and area targets.
Learning Local Control Barrier Functions for Hybrid Systems
Our approach is computationally efficient, minimally invasive to any reference controller, and applicable to large-scale systems.
Reflected Schrödinger Bridge for Constrained Generative Modeling
Diffusion models have become the go-to method for large-scale generative models in real-world applications.
Generating Handwritten Mathematical Expressions From Symbol Graphs: An End-to-End Pipeline
To alleviate this challenge we propose a novel end-to-end G2I generation pipeline (i. e. graph - layout - mask - image) which requires no real masks or nondifferentiable alignment between layouts and masks.
DualFluidNet: an Attention-based Dual-pipeline Network for FLuid Simulation
Fluid motion can be considered as a point cloud transformation when using the SPH method.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms.
Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit
Transformer requires a fixed number of layers and heads which makes them inflexible to the complexity of individual samples and expensive in training and inference.
FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation
Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server.
Interpreting Differentiable Latent States for Healthcare Time-series Data
Understanding the meaning of latent states is crucial for interpreting machine learning models, assuming they capture underlying patterns.
Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering
During low resolution or far away rendering, the pixel size of the image can fall below the Nyquist frequency compared to the screen size of each splatted 3D Gaussian and leads to aliasing effect.
SCALAR-NeRF: SCAlable LARge-scale Neural Radiance Fields for Scene Reconstruction
We enhance the overlapping regions across different blocks by scaling up the bounding boxes of each local block.
Scaling Law of Large Sequential Recommendation Models
We find that scaling up the model size can greatly boost the performance on these challenging tasks, which again verifies the benefits of large recommendation models.
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems.
VQPy: An Object-Oriented Approach to Modern Video Analytics
Video analytics is widely used in contemporary systems and services.
Electric Vehicle Aggregation Review: Benefits and Vulnerabilities of Managing a Growing EV Fleet
Electric vehicles (EVs) are becoming more popular within the United States, making up an increasingly large portion of the US's electricity consumption.
GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding
Specifically, we design two novel algorithms that utilise both the graph structure of the online conversation as well as the semantic information from individual posts for retrieving relevant context nodes from the whole conversation.
HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models
We develop 4 different prompts based on task description, hate definition, few-shot demonstrations and chain-of-thoughts for comprehensive experiments and conduct experiments on open-source LLMs such as LLaMA-1, LLaMA-2 chat, Vicuna as well as OpenAI's GPT-3. 5.
LocSelect: Target Speaker Localization with an Auditory Selective Hearing Mechanism
The prevailing noise-resistant and reverberation-resistant localization algorithms primarily emphasize separating and providing directional output for each speaker in multi-speaker scenarios, without association with the identity of speakers.
A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information
Time series forecasting represents a significant and challenging task across various fields.
On the Equivalence of Graph Convolution and Mixup
We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.
Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection
Numerous companies have started offering services based on large language models (LLM), such as ChatGPT, which inevitably raises privacy concerns as users' prompts are exposed to the model provider.
LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models
As a result, their performance suffers drastically on inputs longer than those encountered during training, substantially limiting their applications in real-world tasks involving long contexts such as encoding scientific articles, code repositories, or long dialogues.
Rotation-Invariant Completion Network
To assess the performance of RICNet and existing methods on point clouds with various poses, we applied random transformations to the point clouds in the MVP dataset and conducted experiments on them.
DReg-NeRF: Deep Registration for Neural Radiance Fields
Although Neural Radiance Fields (NeRF) is popular in the computer vision community recently, registering multiple NeRFs has yet to gain much attention.
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications.
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk.
MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction
In this work, we propose a novel prompt tuning approach with Mixed Prompts for few-shot Attribute Value Extraction, namely MixPAVE.
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods.
A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications
Determining the drivable area, or free space segmentation, is critical for mobile robots to navigate indoor environments safely.
NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection
Next, we design a phoneme-viseme awareness module for cross-modal feature fusion and representation alignment, so that the modality gap can be reduced and the intrinsic complementarity of the two modalities can be better explored.
Inference and Sampling of Point Processes from Diffusion Excursions
In this framework, we relate the return times of a diffusion in a continuous path space to new arrivals of the point process.
Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes
Then we proposed a robust and computationally-efficient method modified from MLE that does not rely on the prior estimation of the heterogeneous intensity and is thus applicable in a data-limited regime (e. g., few-shot, no repeated observations).
AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes
We propose attribute-aware multimodal entity linking, where the input is a mention described with a text and image, and the goal is to predict the corresponding target entity from a multimodal knowledge base (KB) where each entity is also described with a text description, a visual image and a set of attributes and values.
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality
Along with this, we propose novel negative mining techniques in the scene graph space for improving attribute binding and relation understanding.
Ranked #1 on
Image Retrieval
on CREPE (Compositional REPresentation Evaluation)
(Recall@1 (HN-Comp, UC) metric)
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.
A Lightweight Recurrent Learning Network for Sustainable Compressed Sensing
While the initial reconstruction sub-network has a hierarchical structure to progressively recover the image, reducing the number of parameters, the residual reconstruction sub-network facilitates recurrent residual feature extraction via recurrent learning to perform both feature fusion and deep reconstructions across different scales.
Scalable Multiple Patterning Layout Decomposition Implemented by a Distribution Evolutionary Algorithm
As the feature size of semiconductor technology shrinks to 10 nm and beyond, the multiple patterning lithography (MPL) attracts more attention from the industry.
MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding
In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns.
Data-Driven Safe Controller Synthesis for Deterministic Systems: A Posteriori Method With Validation Tests
We first formulate the safety synthesis problem as a robust convex program (RCP) based on notion of control barrier function.
DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields
Recent works such as BARF and GARF can bundle adjust camera poses with neural radiance fields (NeRF) which is based on coordinate-MLPs.
AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion
Despite the impressive results achieved by many existing Structure from Motion (SfM) approaches, there is still a need to improve the robustness, accuracy, and efficiency on large-scale scenes with many outlier matches and sparse view graphs.
You Don't Know When I Will Arrive: Unpredictable Controller Synthesis for Temporal Logic Tasks
This problem is particularly challenging since future information is involved in the synthesis process.
Entropy Rate Maximization of Markov Decision Processes under Linear Temporal Logic Tasks
Then based on a new state classification method, we show the entropy rate maximization problem under LTL task can be effectively solved in polynomial-time.
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition
Noisy Student Training (NST) has recently demonstrated extremely strong performance in Automatic Speech Recognition(ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecasting
Analyzing both temporal and spatial patterns for an accurate forecasting model for financial time series forecasting is a challenge due to the complex nature of temporal-spatial dynamics: time series from different locations often have distinct patterns; and for the same time series, patterns may vary as time goes by.
Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant
The COVID-19 pandemic has been ongoing for nearly two and half years, and new variants of concern (VOCs) of SARS-CoV-2 continue to emerge, which urges the development of broadly neutralizing antibodies.
Predictive Scale-Bridging Simulations through Active Learning
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements.
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning
Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing.
Spacecraft depth completion based on the gray image and the sparse depth map
In this way, the background interference to foreground spacecraft depth completion is effectively avoided.
A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation
A dual-head mechanism is used to predict optical flow for rigid and non-rigid motion based on a divide-and-conquer manner, which significantly improves the optical flow estimation performance.
DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication
The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools.
Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition
Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur.
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
We study reinforcement learning with linear function approximation where the transition probability and reward functions are linear with respect to a feature mapping $\boldsymbol{\phi}(s, a)$.
Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection
In recent years, deep learning-based approaches for visual-inertial odometry (VIO) have shown remarkable performance outperforming traditional geometric methods.
A Distribution Evolutionary Algorithm for the Graph Coloring Problem
Unlike existing estimation of distribution algorithms where a probability model is updated by generated solutions, DEA-PPM employs a distribution population based on a novel probability model, and an orthogonal exploration strategy is introduced to search the distribution space with the assistance of an refinement strategy.
Fast fluorescence lifetime imaging analysis via extreme learning machine
As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches.
Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction
Moreover, to estimate CVR upon the freshly observed but biased distribution with fake negatives, the importance sampling is widely used to reduce the distribution bias.
Compact Graph Structure Learning via Mutual Information Compression
Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views.
CdtGRN: Construction of qualitative time-delayed gene regulatory networks with a deep learning method
Therefore, it is of great significance to elucidate the regulation mechanism over time points.
Influence of Binomial Crossover on Approximation Error of Evolutionary Algorithms
Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principle of DE algorithms.
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning
X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings.
Photonic-enabled radio-frequency self-interference cancellation incorporated in an in-band full-duplex radio-over-fiber system
A photonic approach for radio-frequency (RF) self-interference cancellation (SIC) incorporated in an in-band full-duplex radio-over-fiber system is proposed.
Model-based Decision Making with Imagination for Autonomous Parking
The proposed algorithm consists of three parts: an imaginative model for anticipating results before parking, an improved rapid-exploring random tree (RRT) for planning a feasible trajectory from a given start point to a parking lot, and a path smoothing module for optimizing the efficiency of parking tasks.
A Cuckoo Quantum Evolutionary Algorithm for the Graph Coloring Problem
Based on the framework of the quantum-inspired evolutionary algorithm, a cuckoo quantum evolutionary algorithm (CQEA) is proposed for solving the graph coloring problem (GCP).
Method Towards CVPR 2021 Image Matching Challenge
This report describes Megvii-3D team's approach towards CVPR 2021 Image Matching Workshop.
Energy-based Unknown Intent Detection with Data Manipulation
Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set.
CLIP2Video: Mastering Video-Text Retrieval via Image CLIP
We present CLIP2Video network to transfer the image-language pre-training model to video-text retrieval in an end-to-end manner.
Ranked #13 on
Video Retrieval
on VATEX
(using extra training data)
Deep Learning in Latent Space for Video Prediction and Compression
The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain.
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).
Deep Learning on Graphs for Natural Language Processing
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i. e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems.
VeniBot: Towards Autonomous Venipuncture with Automatic Puncture Area and Angle Regression from NIR Images
For the automation, we focus on the positioning part and propose a Dual-In-Dual-Out network based on two-step learning and two-task learning, which can achieve fully automatic regression of the suitable puncture area and angle from near-infrared(NIR) images.
VeniBot: Towards Autonomous Venipuncture with Semi-supervised Vein Segmentation from Ultrasound Images
In the modern medical care, venipuncture is an indispensable procedure for both diagnosis and treatment.
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN
GAN-based neural vocoders, such as Parallel WaveGAN and MelGAN have attracted great interest due to their lightweight and parallel structures, enabling them to generate high fidelity waveform in a real-time manner.
Generic Perceptual Loss for Modeling Structured Output Dependencies
We hope that this simple, extended perceptual loss may serve as a generic structured-output loss that is applicable to most structured output learning tasks.
Continual Density Ratio Estimation in an Online Setting
To the best of our knowledge, there is no existing method that can evaluate generative models in continual learning without storing samples from the original distribution.
An Optimized H.266/VVC Software Decoder On Mobile Platform
As the successor of H. 265/HEVC, the new versatile video coding standard (H. 266/VVC) can provide up to 50% bitrate saving with the same subjective quality, at the cost of increased decoding complexity.
The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network
In an order-driven financial market, the price of a financial asset is discovered through the interaction of orders - requests to buy or sell at a particular price - that are posted to the public limit order book (LOB).
Signal identification with Kalman Filter towards background-free neutrinoless double beta decay searches in gaseous detectors
Particle tracks and differential energy loss measured in high pressure gaseous detectors can be exploited for event identification in neutrinoless double beta decay~($0\nu \beta \beta$) searches.
Instrumentation and Detectors High Energy Physics - Experiment
Removing leakage-induced correlated errors in superconducting quantum error correction
Quantum computing can become scalable through error correction, but logical error rates only decrease with system size when physical errors are sufficiently uncorrelated.
Quantum Physics
Exponential suppression of bit or phase flip errors with repetitive error correction
QEC also requires that the errors are local and that performance is maintained over many rounds of error correction, two major outstanding experimental challenges.
Quantum Physics
Polyphone Disambiguation in Mandarin Chinese with Semi-Supervised Learning
In this paper, we propose a novel semi-supervised learning (SSL) framework for Mandarin Chinese polyphone disambiguation that can potentially leverage unlimited unlabeled text data.
Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach
We push the envelope of rotation averaging by leveraging the advantages of a global RA method and a local RA method.
Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph
Food recommendation has become an important means to help guide users to adopt healthy dietary habits.
Slow Control System for PandaX-III experiment
The PandaX-III experiment uses high pressure gaseous time projection chamber to search for the neutrinoless double beta decay of $^{136}$Xe.
Anomaly Detection
High Energy Physics - Experiment
Instrumentation and Detectors
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Despite the recent successes of deep learning in natural language processing (NLP), there remains widespread usage of and demand for techniques that do not rely on machine learning.
Entropy Linear Response Theory with Non-Markovian Bath
A non-monotonic behavior of Renyi entropy for fermionic systems is found to be quite general when the environment's temperature is lower.
High Energy Physics - Theory Quantum Gases Strongly Correlated Electrons
Deep reinforcement learning for RAN optimization and control
Due to the high variability of the traffic in the radio access network (RAN), fixed network configurations are not flexible enough to achieve optimal performance.
Observation of separated dynamics of charge and spin in the Fermi-Hubbard model
Strongly correlated quantum systems give rise to many exotic physical phenomena, including high-temperature superconductivity.
Quantum Physics
Simple Neighborhood Representative Pre-processing Boosts Outlier Detectors
Over the decades, traditional outlier detectors have ignored the group-level factor when calculating outlier scores for objects in data by evaluating only the object-level factor, failing to capture the collective outliers.
On Efficient Constructions of Checkpoints
Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models.
Discriminative Representation Loss (DRL): A More Efficient Approach than Gradient Re-Projection in Continual Learning
The use of episodic memories in continual learning has been shown to be effective in terms of alleviating catastrophic forgetting.
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.
Train Your Data Processor: Distribution-Aware and Error-Compensation Coordinate Decoding for Human Pose Estimation
Recently, the leading performance of human pose estimation is dominated by heatmap based methods.
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedding.
Semi-Discriminative Representation Loss for Online Continual Learning
The use of episodic memory in continual learning has demonstrated effectiveness for alleviating catastrophic forgetting.
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN
However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries.
Minor Privacy Protection Through Real-time Video Processing at the Edge
The collection of a lot of personal information about individuals, including the minor members of a family, by closed-circuit television (CCTV) cameras creates a lot of privacy concerns.
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers.
Ranked #3 on
KG-to-Text Generation
on WebQuestions
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
For problems defined on our hardware graph we obtain an approximation ratio that is independent of problem size and observe, for the first time, that performance increases with circuit depth.
Quantum Physics
Automatic, Dynamic, and Nearly Optimal Learning Rate Specification by Local Quadratic Approximation
In each update step, given the gradient direction, we locally approximate the loss function by a standard quadratic function of the learning rate.
I-ViSE: Interactive Video Surveillance as an Edge Service using Unsupervised Feature Queries
Adopting unsupervised methods that do not reveal any private information, the I-ViSE scheme utilizes general features of a human body and color of clothes.
Deform-GAN:An Unsupervised Learning Model for Deformable Registration
Deformable registration is one of the most challenging task in the field of medical image analysis, especially for the alignment between different sequences and modalities.
Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements through Case Studies
Traffic evacuation plays a critical role in saving lives in devastating disasters such as hurricanes, wildfires, floods, earthquakes, etc.
Multi-label Relation Modeling in Facial Action Units Detection
The proposed method consists of the data preprocessing, the feature extraction and the AU classification.
Exploitation and Exploration Analysis of Elitist Evolutionary Algorithms: A Case Study
Known as two cornerstones of problem solving by search, exploitation and exploration are extensively discussed for implementation and application of evolutionary algorithms (EAs).
Occlum: Secure and Efficient Multitasking Inside a Single Enclave of Intel SGX
SFI is a software instrumentation technique for sandboxing untrusted modules (called domains).
Operating Systems Hardware Architecture Cryptography and Security
Graph-Based Parallel Large Scale Structure from Motion
After local reconstructions, we construct a minimum spanning tree (MinST) to find accurate similarity transformations.
Microchain: a Light Hierarchical Consensus Protocol for IoT System
While the large-scale Internet of Things (IoT) makes many new applications feasible, like Smart Cities, IoT also brings new concerns on data reliability, security, and privacy.
Distributed, Parallel, and Cluster Computing
Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning
We then use the trained models to classify and predict potentially high-risk admissions/patients.
Deep Iterative and Adaptive Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously.
Bundle Adjustment Revisited
3D reconstruction has been developing all these two decades, from moderate to medium size and to large scale.
Learning Non-Markovian Quantum Noise from Moiré-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm
In this work, we derive the non-Markovian dynamics between TLS and qubits during a SWAP-like two-qubit gate and the associated average gate fidelity for frequency-tunable Transmon qubits.
Fast and Incremental Loop Closure Detection Using Proximity Graphs
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems.
Identification of Interaction Clusters Using a Semi-supervised Hierarchical Clustering Method
Motivation: Identifying interaction clusters of large gene regulatory networks (GRNs) is critical for its further investigation, while this task is very challenging, attributed to data noise in experiment data, large scale of GRNs, and inconsistency between gene expression profiles and function modules, etc.
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
Natural question generation (QG) aims to generate questions from a passage and an answer.
Iterative Deep Graph Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly learning graph structure and graph embedding simultaneously.
Continual Density Ratio Estimation (CDRE): A new method for evaluating generative models in continual learning
We propose a new method Continual Density Ratio Estimation (CDRE), which can estimate density ratios between a target distribution of real samples and a distribution of samples generated by a model while the model is changing over time and the data of the target distribution is not available after a certain time point.
Microchain: A Hybrid Consensus Mechanism for Lightweight Distributed Ledger for IoT
In this paper, Microchain, based on a hybrid Proof-of-Credit (PoC)-Voting-based Chain Finality (VCF) consensus protocol, is proposed to provide a secure, scalable and lightweight distributed ledger for IoT systems.
Distributed, Parallel, and Cluster Computing
I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making
This paper presents a forensic surveillance strategy by introducing an Instant Suspicious Activity identiFication at the Edge (I-SAFE) using fuzzy decision making.