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Found 54 papers, 10 papers with code
Preventing the Popular Item Embedding Based Attack in Federated Recommendations
The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items.
L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression
Learning-based probabilistic models can be combined with an entropy coder for data compression.
MILLION: A General Multi-Objective Framework with Controllable Risk for Portfolio Management
Subsequently, in the risk control phase, we propose two methods, i. e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level.
Efficient Data-aware Distance Comparison Operations for High-Dimensional Approximate Nearest Neighbor Search
To achieve this, we propose an Data-Aware Distance Estimation approach, called DADE, which approximates the exact distance in a lower-dimensional space.
Conditional Lagrangian Wasserstein Flow for Time Series Imputation
To overcome the limitations of diffusion model-based imputation methods, e. g., slow convergence in inference, we propose a novel method for time series imputation in this work, called Conditional Lagrangian Wasserstein Flow.
Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks
However, large memory footprint and high energy consumption during the training process excludes the low-end devices from contributing to the global model with their own data, which severely deteriorates the model performance in real-world scenarios.
AI-Driven Strategies for Reducing Student Withdrawal -- A Study of EMU Student Stopout
In December 2013, there were 29 million people with some college education but no degree.
Temporal Label Hierachical Network for Compound Emotion Recognition
The emotion recognition has attracted more attention in recent decades.
Learning to Adapt Foundation Model DINOv2 for Capsule Endoscopy Diagnosis
Our method, inspired by the DINOv2 foundation model, applies low-rank adaptation learning to tailor foundation models for capsule endoscopy diagnosis effectively.
Improvement of Applicability in Student Performance Prediction Based on Transfer Learning
This study proposes a method to improve prediction accuracy by employing transfer learning techniques on the dataset with varying distributions.
A Practice in Enrollment Prediction with Markov Chain Models
Enrollment projection is a critical aspect of university management, guiding decisions related to resource allocation and revenue forecasting.
LightTR: A Lightweight Framework for Federated Trajectory Recovery
Due to the limited acquisition capabilities of edge devices, a lot of trajectories are recorded at a low sampling rate, which may lead to the effectiveness drop of urban applications.
A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data
The widespread deployment of wireless and mobile devices results in a proliferation of spatio-temporal data that is used in applications, e. g., traffic prediction, human mobility mining, and air quality prediction, where spatio-temporal prediction is often essential to enable safety, predictability, or reliability.
E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series
We propose E2Usd that enables efficient-yet-accurate unsupervised MTS state detection.
LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System
The natural language understanding capability has always been a barrier to the intent recognition performance of the Knowledge-Based-Question-and-Answer (KBQA) system, which arises from linguistic diversity and the newly appeared intent.
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition
These segment-level patches are then encoded using a stack of Swin blocks, in which a local window Transformer is utilized to explore local inter-frame emotional information across frame patches of each segment patch.
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation
In speaker-independent speech emotion recognition, the training and testing samples are collected from diverse speakers, leading to a multi-domain shift challenge across the feature distributions of data from different speakers.
Audio Prompt Tuning for Universal Sound Separation
Specifically, APT improves the separation performance of specific sources through training a small number of prompt parameters with limited audio samples, while maintaining the generalization of the USS model by keeping its parameters frozen.
RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery
RAEs are adversarial perturbed images that can be restored to the original.
Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly
Shape assembly aims to reassemble parts (or fragments) into a complete object, which is a common task in our daily life.
Separate Anything You Describe
In this work, we introduce AudioSep, a foundation model for open-domain audio source separation with natural language queries.
Improving the Generalization Ability in Essay Coherence Evaluation through Monotonic Constraints
To address these concerns, we propose a coherence scoring model consisting of a regression model with two feature extractors: a local coherence discriminative model and a punctuation correction model.
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.
Learning Personalized Page Content Ranking Using Customer Representation
We propose a deep learning based bandit algorithm that incorporates historical shopping behavior, customer latent shopping goals, and the correlation between customers and content categories.
LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
Many deep learning models have been proposed to improve the accuracy of CTS forecasting.
Ranked #1 on
Traffic Prediction
on PeMS04
(FLOPs(M) metric, using extra
training data)
Computational Efficiency
Correlated Time Series Forecasting
+4
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition
In this paper, we propose a novel deep transfer learning method called deep implicit distribution alignment networks (DIDAN) to deal with cross-corpus speech emotion recognition (SER) problem, in which the labeled training (source) and unlabeled testing (target) speech signals come from different corpora.
Kainate receptor modulation by NETO2
Glutamate-gated kainate receptors (KARs) are ubiquitous in the central nervous system of vertebrates, mediate synaptic transmission on post-synapse, and modulate transmitter release on pre-synapse.
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network
The F-Encoder and T-Encoder model the correlations within frequency bands and time frames, respectively, and they are embedded into a time-frequency joint learning strategy to obtain the time-frequency patterns for speech emotions.
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis
The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energy management, where anomaly detection is often essential to enable reliability and safety.
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey
A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices.
Uncertainty Quantification for Traffic Forecasting: A Unified Approach
To achieve this, we develop Deep Spatio-Temporal Uncertainty Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic uncertainty.
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation
It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments.
Intra Encoding Complexity Control with a Time-Cost Model for Versatile Video Coding
Inspired by rate control algorithms, we propose a scheme to precisely control the intra encoding complexity of VVC.
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
Speech restoration aims to remove distortions in speech signals.
Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version
This is an extended version of "Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection", to appear in IEEE ICDE 2022.
Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery
Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearance, obvious intraclass variation, small size and serious background's interference.
Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks
Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input.
4-D Epanechnikov Mixture Regression in Light Field Image Compression
A 4-D Epanechnikov mixture regression (4-D EMR) was proposed based on this 4-D EK, and a 4-D adaptive model selection (4-D AMLS) algorithm was designed to realize the optimal modeling for a pseudo video sequence (PVS) of the extracted key-EIA.
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
Generalized Nash Equilibrium Seeking Algorithm Design for Distributed Constrained Multi-Cluster Games
The multi-cluster games are addressed in this paper, where all players team up with the players in the cluster that they belong to, and compete against the players in other clusters to minimize the cost function of their own cluster.
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions
Monitoring complex systems results in massive multivariate time series data, and anomaly detection of these data is very important to maintain the normal operation of the systems.
Three-dimensional Epanechnikov mixture regression in image coding
We propose a three-dimensional (3-D) Epanechnikov Mixture Regression (EMR) based on our Epanechnikov Kernel (EK) and realize a complete framework for image coding.
A probabilistic model for missing traffic volume reconstruction based on data fusion
Moreover, for the low coverage problem, which cannot be handled by most existing methods, the proposed model can also achieve high accuracy.
Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition
The adaptive adjusting term is composed of two complementary factors: 1) quantity factor, which pays more attention to tail classes, and 2) difficulty factor, which adaptively pays more attention to hard instances in the training process.
A Lightweight Approach of Human-Like Playtesting
This paper presents LIT -- our lightweight approach to generalize playtesting tactics from manual testing, and to adopt the generalized tactics to automate game testing.
Software Engineering
Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations
To address these problems, we propose a Stratified and Time-aware Sampling based Adaptive Ensemble Learning framework, called STS-AEL, to improve the accuracy of streaming recommendations.
Double-Wing Mixture of Experts for Streaming Recommendations
In VRS-DWMoE, we first devise variational and reservoir-enhanced sampling to wisely complement new data with historical data, and thus address the user preference drift issue while capturing long-term user preferences.
From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generation
We introduce a new task named Story Ending Generation (SEG), whic-h aims at generating a coherent story ending from a sequence of story plot.
Multi-Perspective Fusion Network for Commonsense Reading Comprehension
From the experimental results, we can conclude that the difference fusion is comparable with union fusion, and the similarity fusion needs to be activated by the union fusion.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention
To comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind.
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature.
Uplift Modeling with Multiple Treatments and General Response Types
It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved.
