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Found 147 papers, 39 papers with code
PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language Detection
This paper describes the second-placed system for subtask 2 and the ninth-placed system for subtask 1 in SemEval 2022 Task 4: Patronizing and Condescending Language Detection.
Seedream 3.0 Technical Report
At the data stratum, we double the dataset using a defect-aware training paradigm and a dual-axis collaborative data-sampling framework.
TimeZero: Temporal Video Grounding with Reasoning-Guided LVLM
We introduce TimeZero, a reasoning-guided LVLM designed for the temporal video grounding (TVG) task.
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model
To address these limitations, we present Seedream 2. 0, a native Chinese-English bilingual image generation foundation model that excels across diverse dimensions, which adeptly manages text prompt in both Chinese and English, supporting bilingual image generation and text rendering.
Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in QA Agents
Using a dataset of questions about a Samsung Smart TV user manual, we demonstrate that synthetic data generated by LLMs outperforms crowdsourced data in reducing hallucination in finetuned models.
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their extensive knowledge and reasoning.
Smoothed Embeddings for Robust Language Models
Improving the safety and reliability of large language models (LLMs) is a crucial aspect of realizing trustworthy AI systems.
Low-rank Prompt Interaction for Continual Vision-Language Retrieval
Considering that the training parameters scale to the number of layers and tasks, we propose low-rank interaction-augmented decomposition to avoid memory explosion while enhancing the cross-modal association through sharing and separating common-specific low-rank factors.
Learning to Adapt to Low-Resource Paraphrase Generation
LAPA has three-stage training on three types of related resources to solve this problem: 1. pre-training PLMs on unsupervised corpora, 2. inserting an adapter layer and meta-training on source domain labeled data, and 3. fine-tuning adapters on a small amount of target domain labeled data.
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration
In this work, we address the limitations of denoising diffusion models (DDMs) in image restoration tasks, particularly the shape and color distortions that can compromise image quality.
Distributed satellite information networks: Architecture, enabling technologies, and trends
In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services.
Neighborhood Commonality-aware Evolution Network for Continuous Generalized Category Discovery
Continuous Generalized Category Discovery (C-GCD) aims to continually discover novel classes from unlabelled image sets while maintaining performance on old classes.
Quantum Diffusion Models for Few-Shot Learning
Modern quantum machine learning (QML) methods involve the variational optimization of parameterized quantum circuits on training datasets, followed by predictions on testing datasets.
On Calibration of LLM-based Guard Models for Reliable Content Moderation
Large language models (LLMs) pose significant risks due to the potential for generating harmful content or users attempting to evade guardrails.
When Attention Sink Emerges in Language Models: An Empirical View
In this work, we first demonstrate that attention sinks exist universally in LMs with various inputs, even in small models.
Quo Vadis, Motion Generation? From Large Language Models to Large Motion Models
Inspired by the recent success of LLMs, the field of human motion understanding has increasingly shifted towards the development of large motion models.
Convolutional Dictionary Learning Based Hybrid-Field Channel Estimation for XL-RIS-Aided Massive MIMO Systems
The CDL formulation is cast as a bilevel optimization problem, which we solve using a gradient-based approach.
Knowledge Adaptation Network for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to incrementally recognize new classes using a few samples while maintaining the performance on previously learned classes.
class-incremental learning
Few-Shot Class-Incremental Learning
+1
Exploring User-level Gradient Inversion with a Diffusion Prior
We explore user-level gradient inversion as a new attack surface in distributed learning.
Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
This approach manipulates a pre-trained language model to increase the leakage of private data during the fine-tuning process.
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
While previous work has studied various privacy risks of sharing gradients, our paper aims to provide a systematic approach to analyze private information leakage from gradients.
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language Models
Further analysis on cross-model transfer indicates the pivotal role of first target token optimization in leveraging suffix transferability for efficient searching.
Unifying Multitrack Music Arrangement via Reconstruction Fine-Tuning and Efficient Tokenization
Automatic music arrangement streamlines the creation of musical variants for composers and arrangers, reducing reliance on extensive music expertise.
Quantifying the Blockchain Trilemma: A Comparative Analysis of Algorand, Ethereum 2.0, and Beyond
Blockchain technology is essential for the digital economy and metaverse, supporting applications from decentralized finance to virtual assets.
Variational Randomized Smoothing for Sample-Wise Adversarial Robustness
Randomized smoothing is a defensive technique to achieve enhanced robustness against adversarial examples which are small input perturbations that degrade the performance of neural network models.
GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM
Large vision-language models (LVLMs), such as the Generative Pre-trained Transformer 4-omni (GPT-4o), are emerging multi-modal foundation models which have great potential as powerful artificial-intelligence (AI) assistance tools for a myriad of applications, including healthcare, industrial, and academic sectors.
Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals
Notably, the proposed method is also robust to an increase in the number of missing channels compared to other methods.
KUNPENG: An Embodied Large Model for Intelligent Maritime
Intelligent maritime, as an essential component of smart ocean construction, deeply integrates advanced artificial intelligence technology and data analysis methods, which covers multiple aspects such as smart vessels, route optimization, safe navigation, aiming to enhance the efficiency of ocean resource utilization and the intelligence of transportation networks.
Controllable Conversations: Planning-Based Dialogue Agent with Large Language Models
Conversational agents powered by Large Language Models (LLMs) show superior performance in various tasks.
QuadrupedGPT: Towards a Versatile Quadruped Agent in Open-ended Worlds
As robotic agents increasingly assist humans in reality, quadruped robots offer unique opportunities for interaction in complex scenarios due to their agile movement.
Efficient Low-rank Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization
We prove the global convergence of the proposed algorithm, guaranteeing that every limit point of the iterates is a critical point.
EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration
Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem.
Joint Observer Gain and Input Design for Asymptotic Active Fault Diagnosis
This paper proposes a joint gain and input design method for observer-based asymptotic active fault diagnosis, which is based on a newly-defined notion named the excluding degree of the origin from a zonotope.
Efficient Differentially Private Fine-Tuning of Diffusion Models
The recent developments of Diffusion Models (DMs) enable generation of astonishingly high-quality synthetic samples.
Pitch-Aware RNN-T for Mandarin Chinese Mispronunciation Detection and Diagnosis
Mispronunciation Detection and Diagnosis (MDD) systems, leveraging Automatic Speech Recognition (ASR), face two main challenges in Mandarin Chinese: 1) The two-stage models create an information gap between the phoneme or tone classification stage and the MDD stage.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation
Comprehensive experiments show that TPAR outperforms SOTA methods on the link prediction task for both the interpolation and the extrapolation settings.
End-to-End Real-World Polyphonic Piano Audio-to-Score Transcription with Hierarchical Decoding
Piano audio-to-score transcription (A2S) is an important yet underexplored task with extensive applications for music composition, practice, and analysis.
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties.
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models
To guide video generation with the additional image input, we propose a "repeat-and-slide" strategy that modulates the reverse denoising process, allowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image.
Prompt-tuning for Clickbait Detection via Text Summarization
To address this problem, we propose a prompt-tuning method for clickbait detection via text summarization in this paper, text summarization is introduced to summarize the contents, and clickbait detection is performed based on the similarity between the generated summary and the contents.
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class Discovery
To counteract this inefficiency, we opt to cluster only the unlabelled instances and subsequently expand the cluster prototypes with our introduced potential prototypes to fast explore novel classes.
Contouring Error Bounded Control for Biaxial Switched Linear Systems
To address the need for improved contouring accuracy in industrial machines with position-dependent structural flexibility, this paper introduces a novel contouring error-bounded control algorithm for biaxial switched linear systems.
Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation
Second, we show how our framework effectively mimics empathic lead user interviews, identifying a greater number of latent needs than conventional human interviews.
Probabilistic reachable sets of stochastic nonlinear systems with contextual uncertainties
The existing methods of computing probabilistic reachable sets normally assume that stochastic uncertainties are independent of system states, inputs, and other environment variables.
SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules
Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision.
OSTAF: A One-Shot Tuning Method for Improved Attribute-Focused T2I Personalization
Personalized text-to-image (T2I) models not only produce lifelike and varied visuals but also allow users to tailor the images to fit their personal taste.
AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs
High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype hardware designs rapidly.
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
In this paper, we propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem during federated learning.
UniCode: Learning a Unified Codebook for Multimodal Large Language Models
In this paper, we propose \textbf{UniCode}, a novel approach within the domain of multimodal large language models (MLLMs) that learns a unified codebook to efficiently tokenize visual, text, and potentially other types of signals.
Generative Retrieval with Large Language Models
When completing knowledge-intensive tasks, humans sometimes need not just an answer but also a corresponding reference passage for auxiliary reading.
TaylorGrid: Towards Fast and High-Quality Implicit Field Learning via Direct Taylor-based Grid Optimization
In this paper, we aim for both fast and high-quality implicit field learning, and propose TaylorGrid, a novel implicit field representation which can be efficiently computed via direct Taylor expansion optimization on 2D or 3D grids.
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
In practical applications, such a worst-case guarantee may be overkill: practical attackers may lack exact knowledge of (nearly all of) the private data, and our data set might be easier to defend, in some sense, than the worst-case data set.
Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
A multimodal large language model (MLLM) agent can receive instructions, capture images, retrieve histories from memory, and decide which tools to use.
Benchmarking Large Language Models on Communicative Medical Coaching: a Novel System and Dataset
Traditional applications of natural language processing (NLP) in healthcare have predominantly focused on patient-centered services, enhancing patient interactions and care delivery, such as through medical dialogue systems.
3D-SSGAN: Lifting 2D Semantics for 3D-Aware Compositional Portrait Synthesis
Conversely, some GAN-based 2D portrait synthesis methods can achieve clear disentanglement of facial regions, but they cannot preserve view consistency due to a lack of 3D modeling abilities.
P2M2-Net: Part-Aware Prompt-Guided Multimodal Point Cloud Completion
Inferring missing regions from severely occluded point clouds is highly challenging.
MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models
The emergence of various medical large language models (LLMs) in the medical domain has highlighted the need for unified evaluation standards, as manual evaluation of LLMs proves to be time-consuming and labor-intensive.
Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling
In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges.
GenKIE: Robust Generative Multimodal Document Key Information Extraction
Key information extraction (KIE) from scanned documents has gained increasing attention because of its applications in various domains.
Advancing Test-Time Adaptation in Wild Acoustic Test Settings
In this work, we propose a novel wild acoustic TTA method tailored for ASR fine-tuned acoustic foundation models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning
Large Language models (LLMs) possess the capability to engage In-context Learning (ICL) by leveraging a few demonstrations pertaining to a new downstream task as conditions.
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects.
On Memorization in Diffusion Models
Looking into this, we first observe that memorization behaviors tend to occur on smaller-sized datasets, which motivates our definition of effective model memorization (EMM), a metric measuring the maximum size of training data at which a learned diffusion model approximates its theoretical optimum.
ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks
Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs).
What Determines the Price of NFTs?
In the evolving landscape of digital art, Non-Fungible Tokens (NFTs) have emerged as a groundbreaking platform, bridging the realms of art and technology.
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
To this end, and capitalizing on the powerful fine-grained generative control offered by the recent diffusion-based generative models, we introduce Steered Diffusion, a generalized framework for photorealistic zero-shot conditional image generation using a diffusion model trained for unconditional generation.
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening
Accurate model can achieve high sample efficiency by finding the most promising compounds with only a fraction of the whole library being virtually screened.
Graph Self-Contrast Representation Learning
Further, Since the triplet loss only optimizes the relative distance between the anchor and its positive/negative samples, it is difficult to ensure the absolute distance between the anchor and positive sample.
Stochastic Co-design of Storage and Control for Water Distribution Systems
To address these limitations, we introduce a method to simultaneously design infrastructure and develop control parameters, the co-design problem, with the aim of improving the overall efficiency of the system.
Elucidate Gender Fairness in Singing Voice Transcription
Leveraging the prior knowledge that pitch distributions may contribute to the gender bias, we propose conditionally aligning acoustic representations between demographic groups by feeding note events to the attribute predictor.
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding
It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances.
LOAF-M2L: Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation
Despite previous efforts in melody-to-lyric generation research, there is still a significant compatibility gap between generated lyrics and melodies, negatively impacting the singability of the outputs.
A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling
Motivated by this, we propose a positive-augmentation and positive-mixup positive-unlabeled metric learning framework (P3M).
Document-level RE with incomplete labeling
Metric Learning
+1
Knowledge Transfer-Driven Few-Shot Class-Incremental Learning
Concretely, RESA mimics the real incremental setting and constructs pseudo incremental tasks globally and locally, where the global pseudo incremental tasks are designed to coincide with the learning objective of FSCIL and the local pseudo incremental tasks are designed to improve the model's plasticity, respectively.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Clickbait Detection via Large Language Models
Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media.
Zero-Shot Automatic Pronunciation Assessment
Automatic Pronunciation Assessment (APA) is vital for computer-assisted language learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Songs Across Borders: Singable and Controllable Neural Lyric Translation
The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations.
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning
To address these challenges, we propose Attribute missing Graph Contrastive Learning (AmGCL), a framework for handling missing node attributes in attribute graph data.
MXM-CLR: A Unified Framework for Contrastive Learning of Multifold Cross-Modal Representations
Existing cross-modal contrastive representation learning (XM-CLR) methods such as CLIP are not fully suitable for multifold data as they only consider one positive pair and treat other pairs as negative when computing the contrastive loss.
MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition
However, despite researchers exploring cross-lingual translation techniques such as machine translation and audio speech translation to overcome language barriers, there is still a shortage of cross-lingual studies on visual speech.
A Provably Secure Strong PUF based on LWE: Construction and Implementation
We construct lattice PUF with a physically obfuscated key and an LWE decryption function block.
Exploring Group Video Captioning with Efficient Relational Approximation
In this study, we propose a new task, group video captioning, which aims to infer the desired content among a group of target videos and describe it with another group of related reference videos.
Shape-Aware Fine-Grained Classification of Erythroid Cells
BMEC contains 5, 666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
A Transformer-based Generative Model for De Novo Molecular Design
In the scope of drug discovery, the molecular design aims to identify novel compounds from the chemical space where the potential drug-like molecules are estimated to be in the order of 10^60 - 10^100.
A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling
To solve the common incomplete labeling problem, we propose a unified positive-unlabeled learning framework - shift and squared ranking loss positive-unlabeled (SSR-PU) learning.
Safety-based Speed Control of a Wheelchair using Robust Adaptive Model Predictive Control
Electric-powered wheelchair plays an important role in providing accessibility for people with mobility impairment.
quEEGNet: Quantum AI for Biosignal Processing
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications.
Adversarial Bi-Regressor Network for Domain Adaptive Regression
Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning.
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates
The cyclic-small-gain theorem is used to derive sufficient conditions on the quantization parameters for guaranteeing the stability of the system under a limited data rate.
Improved Pump Setpoint Selection Using a Calibrated Hydraulic Model of a High-Pressure Irrigation System
This paper presents a case study of the operational management of the Robinvale high-pressure piped irrigation water delivery system (RVHPS) in Australia.
Data-driven Predictive Tracking Control based on Koopman Operators
Constraint handling during tracking operations is at the core of many real-world control implementations and is well understood when dynamic models of the underlying system exist, yet becomes more challenging when data-driven models are used to describe the nonlinear system at hand.
Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation
Particularly, the model integrates the macro-level guided-category knowledge and micro-level open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables within the mesoscopic scale.
Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription
To fill in the performance gap between ALT and ASR, we attempt to exploit the similarities between speech and singing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MM-ALT: A Multimodal Automatic Lyric Transcription System
Automatic lyric transcription (ALT) is a nascent field of study attracting increasing interest from both the speech and music information retrieval communities, given its significant application potential.
Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval
To keep independent encoding of questions and answers during inference stage, variational auto-encoder is further introduced to reconstruct answers (questions) from question (answer) embeddings as an auxiliary task to enhance QA interaction in representation learning in training stage.
AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment.
Quantum Transfer Learning for Wi-Fi Sensing
Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment.
Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems
This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem.
Learning to Learn Quantum Turbo Detection
This paper investigates a turbo receiver employing a variational quantum circuit (VQC).
A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising
Unlike previous attention mechanisms that handle pixel-level, channel-level, or patch-level features, MPA focuses on features at the image level.
Exploring Transformer's potential on automatic piano transcription
Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation.
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion
Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.
Ranked #3 on
Link Prediction
on GDELT
The Evolution of Blockchain: from Lit to Dark
A 1% increase in the probability of being frontrun raises users' adoption rate of the dark venue by 0. 6%.
Multi-Band Wi-Fi Sensing with Matched Feature Granularity
The granularity matching is realized by pairing two feature maps from the CSI and beam SNR at different granularity levels and linearly combining all paired feature maps into a fused feature map with learnable weights.
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data
We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label.
MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation
Recent advances in generative adversarial networks (GANs) have led to remarkable achievements in face image synthesis.
CGNN: Traffic Classification with Graph Neural Network
Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification.
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
Generating shapes using natural language can enable new ways of imagining and creating the things around us.
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN
However, it is challenging for GANs to model distributions of separate non-i. i. d.
Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models
Although deep learning models are powerful among various applications, most deep learning models are still a black box, lacking verifiability and interpretability, which means the decision-making process that human beings cannot understand.
PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning
This paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: ReadingComprehension of Abstract Meaning.
STRODE: Stochastic Boundary Ordinary Differential Equation
In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks.
Behavior of Liquidity Providers in Decentralized Exchanges
In this paper, we aim to understand how liquidity providers react to market information and how they benefit from providing liquidity in DEXes.
Personalized Popular Music Generation Using Imitation and Structure
Many practices have been presented in music generation recently.
Cyclic Arbitrage in Decentralized Exchanges
We find that traders have executed 292, 606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue.
Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations
The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos.
Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models
Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks.
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
Minimizing Pumping Energy Cost in Real-time Operations of Water Distribution Systems using Economic Model Predictive Control
In this research, a novel economic model predictive control (EMPC) framework for real-time management of WDSs is proposed.
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users.
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner.
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond.
Robust Machine Learning via Privacy/Rate-Distortion Theory
Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples.
A Biologically Plausible Audio-Visual Integration Model for Continual Learning
Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain.
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory signals and prior knowledge, consequently becoming a recognizable concept or object through coupling and transformation of these percepts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction
This is motivated by the rateless property of conventional PCA, where the least important principal components can be discarded to realize variable rate dimensionality reduction that gracefully degrades the distortion.
Disentangled Adversarial Transfer Learning for Physiological Biosignals
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways.
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities.
Ranked #1 on
Face Alignment
on Menpo
Fake News Detection with Different Models
This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.
Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation
Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts.
Adversarial Deep Learning in EEG Biometrics
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG.
Learning to Modulate for Non-coherent MIMO
The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in place of traditional designs.
Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks
This paper studies a new application of deep learning (DL) for optimizing constellations in two-way relaying with physical-layer network coding (PNC), where deep neural network (DNN)-based modulation and demodulation are employed at each terminal and relay node.
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects.
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location.
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs).
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets.
An optimized system to solve text-based CAPTCHA
Currently, various types of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different segmentation ways.
Invariant Representations from Adversarially Censored Autoencoders
In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to prevent.
English Out-of-Vocabulary Lexical Evaluation Task
Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge.
Privacy-Preserving Adversarial Networks
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information.
Semantic Segmentation with Reverse Attention
Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation.
Ranked #107 on
Semantic Segmentation
on NYU Depth v2
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning.
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings.
