Search Results for author:
Found 482 papers, 145 papers with code
End-to-end Dynamic Matching Network for Multi-view Multi-person 3d Pose Estimation
To address this phenomenon, we propose a novel end-to-end training scheme that brings the three separate modules into a single model.
Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy
Emotion inference in multi-turn conversations aims to predict the participant’s emotion in the next upcoming turn without knowing the participant’s response yet, and is a necessary step for applications such as dialogue planning.
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$.
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder
Detecting public sentiment drift is a challenging task due to sentiment change over time.
TG-LMM: Enhancing Medical Image Segmentation Accuracy through Text-Guided Large Multi-Modal Model
We propose TG-LMM (Text-Guided Large Multi-Modal Model), a novel approach that leverages textual descriptions of organs to enhance segmentation accuracy in medical images.
DS MYOLO: A Reliable Object Detector Based on SSMs for Driving Scenarios
Accurate real-time object detection enhances the safety of advanced driver-assistance systems, making it an essential component in driving scenarios.
Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation
This study proposed a novel deep time series aggregation scheme (DTSAs) to generate typical operational scenarios, considering the large amount of historical operational snapshot data.
Optimal Dispatch Strategy for a Multi-microgrid Cooperative Alliance Using a Two-Stage Pricing Mechanism
To coordinate resources among multi-level stakeholders and enhance the integration of electric vehicles (EVs) into multi-microgrids, this study proposes an optimal dispatch strategy within a multi-microgrid cooperative alliance using a nuanced two-stage pricing mechanism.
P3P: Pseudo-3D Pre-training for Scaling 3D Masked Autoencoders
Inspired by semi-supervised learning leveraging limited labeled data and a large amount of unlabeled data, in this work, we propose a novel self-supervised pre-training framework utilizing the real 3D data and the pseudo-3D data lifted from images by a large depth estimation model.
Flemme: A Flexible and Modular Learning Platform for Medical Images
We propose Flemme, a FLExible and Modular learning platform for MEdical images.
OU-CoViT: Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF Images
Furthermore, the novel architecture of OU-CoViT allows generalizability and extensions of our dual adaptation and Copula Loss to various ViT variants and large DL models on small medical datasets.
Coordinated Spectral Efficiency Prediction for Real-World 5G CoMP Systems
However, characterizing the CSE is intractable due to the inherent complexity of the CoMP channel model and the diversity of the 5G dynamic network environment, which poses a great challenge for CSE prediction in real-world 5G CoMP systems.
Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches
From the generated isometric image, we use a pre-trained image understanding method to segment the image into meaningful parts, such as off-ground objects, trees, and buildings, and extract the 2D scene layout.
A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction
Legal charge prediction, an essential task in legal AI, seeks to assign accurate charge labels to case descriptions, attracting significant recent interest.
Exploring Query Understanding for Amazon Product Search
Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model.
CACE-Net: Co-guidance Attention and Contrastive Enhancement for Effective Audio-Visual Event Localization
Specifically, we have enhanced the model's ability to discern subtle differences between event and background and improved the accuracy of event classification in our model.
FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models
In this paper, we introduce a Fine-tuning Initial Noise Distribution (FIND) framework with policy optimization, which unleashes the powerful potential of pre-trained diffusion networks by directly optimizing the initial distribution to align the generated contents with user-input prompts.
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches
Adversarial attacks against monocular depth estimation (MDE) systems pose significant challenges, particularly in safety-critical applications such as autonomous driving.
LawLuo: A Chinese Law Firm Co-run by LLM Agents
In response to these challenges, we propose a novel legal dialogue framework that leverages the collaborative capabilities of multiple LLM agents, termed LawLuo.
Transcranial low-level laser stimulation in near infrared-II region for brain safety and protection
Background: The use of near-infrared lasers for transcranial photobiomodulation (tPBM) offers a non-invasive method for influencing brain activity and is beneficial for various neurological conditions.
Handling Distance Constraint in Movable Antenna Aided Systems: A General Optimization Framework
The movable antenna (MA) is a promising technology to exploit more spatial degrees of freedom for enhancing wireless system performance.
SEED-Story: Multimodal Long Story Generation with Large Language Model
We further propose multimodal attention sink mechanism to enable the generation of stories with up to 25 sequences (only 10 for training) in a highly efficient autoregressive manner.
UICrit: Enhancing Automated Design Evaluation with a UICritique Dataset
We hypothesize that automatic evaluation can be improved by collecting a targeted UI feedback dataset and then using this dataset to enhance the performance of general-purpose LLMs.
Dy-mer: An Explainable DNA Sequence Representation Scheme using Sparse Recovery
DNA sequences encode vital genetic and biological information, yet these unfixed-length sequences cannot serve as the input of common data mining algorithms.
SAM Fewshot Finetuning for Anatomical Segmentation in Medical Images
With our method, users can manually segment a few 2D slices offline, and the embeddings of these annotated image regions serve as effective prompts for online segmentation tasks.
Deciphering interventional dynamical causality from non-intervention systems
Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies.
Directly Training Temporal Spiking Neural Network with Sparse Surrogate Gradient
Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based computing and energy-efficient features.
Enhancing Resilience of Power Systems against Typhoon Threats: A Hybrid Data-Model Driven Approach
This chapter addresses the increasing vulnerability of coastal regions to typhoons and the consequent power outages, emphasizing the critical role of power transmission systems in disaster resilience.
Spiking Neural Networks with Consistent Mapping Relations Allow High-Accuracy Inference
Spike-based neuromorphic hardware has demonstrated substantial potential in low energy consumption and efficient inference.
Wings: Learning Multimodal LLMs without Text-only Forgetting
Initially, image and text inputs are aligned with visual learners operating alongside the main attention, balancing focus on visual elements.
Parrot: Multilingual Visual Instruction Tuning
In this paper, we introduce Parrot, a novel method that utilizes textual guidance to drive visual token alignment at the language level.
Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning
Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making prototype verification critical to quality assessment.
Ovis: Structural Embedding Alignment for Multimodal Large Language Model
However, the misalignment between two embedding strategies in MLLMs -- the structural textual embeddings based on an embedding look-up table and the continuous embeddings generated directly by the vision encoder -- makes challenges for a more seamless fusion of visual and textual information.
Multimodal Large Language Model
Visual Question Answering (VQA)
Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
Empirically, we conduct a series of experiments on the D4RL benchmark, where A2PR demonstrates state-of-the-art performance.
EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision
EventZoom employs a progressive temporal strategy that intelligently blends time and space to enhance the diversity and complexity of the data while maintaining its authenticity.
Revision Matters: Generative Design Guided by Revision Edits
In contrast, self-revisions that fully rely on model's own judgement, lead to an echo chamber that prevents iterative improvement, and sometimes leads to generative degradation.
Causal-Aware Graph Neural Architecture Search under Distribution Shifts
We propose to handle the distribution shifts in the graph architecture search process by discovering and exploiting the causal relationship between graphs and architectures to search for the optimal architectures that can generalize under distribution shifts.
Devil's Advocate: Anticipatory Reflection for LLM Agents
The experimental results suggest that our introspection-driven approach not only enhances the agent's ability to navigate unanticipated challenges through a robust mechanism of plan execution, but also improves efficiency by reducing the number of trials and plan revisions by 45% needed to achieve a task.
Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
Large language models (LLMs) significantly enhance the performance of various applications, but they are computationally intensive and energy-demanding.
VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
To further reduce stored parameters, we introduce a "divide-and-share" paradigm that breaks the barriers of low-rank decomposition across matrix dimensions, modules and layers by sharing parameters globally via a vector bank.
Natural Language Understanding
parameter-efficient fine-tuning
+1
LAM3D: Large Image-Point-Cloud Alignment Model for 3D Reconstruction from Single Image
Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images.
Quality assurance of organs-at-risk delineation in radiotherapy
Automatic segmentation can be used to reduce the physician workload and improve the consistency.
CoLay: Controllable Layout Generation through Multi-conditional Latent Diffusion
Layout design generation has recently gained significant attention due to its potential applications in various fields, including UI, graphic, and floor plan design.
Leveraging Human Revisions for Improving Text-to-Layout Models
Our method, Revision-Aware Reward Models ($\method$), allows a generative text-to-layout model to produce more modern, designer-aligned layouts, showing the potential for utilizing human revisions and stronger forms of feedback in improving generative models.
ClothPPO: A Proximal Policy Optimization Enhancing Framework for Robotic Cloth Manipulation with Observation-Aligned Action Spaces
In this paper, we introduce ClothPPO, a framework that employs a policy gradient algorithm based on actor-critic architecture to enhance a pre-trained model with huge 10^6 action spaces aligned with observation in the task of unfolding clothes.
Imagine the Unseen: Occluded Pedestrian Detection via Adversarial Feature Completion
From the perspective of reducing intra-class variance, we propose to complete features for occluded regions so as to align the features of pedestrians across different occlusion patterns.
Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective
Specifically, we employ an adversarial search strategy to search for the adversarial embedding which can transfer across different unlearned models.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
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.
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of leading models like GPT4V and Gemini, partly due to a lack of extensive, high-quality instruction tuning data.
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
We verify the effectiveness of the proposed explanations from two aspects: (1) Counterfactual Trace Visualization: the enhanced forgery images are useful to reveal artifacts by visually contrasting the original images and two different visualization methods; (2) Transferable Adversarial Attacks: the adversarial forgery images generated by attacking the detection model are able to mislead other detection models, implying the removed artifacts are general.
PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer
Amidst the surge in deep learning-based password guessing models, challenges of generating high-quality passwords and reducing duplicate passwords persist.
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios.
NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints.
Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane
We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass.
FusionINN: Decomposable Image Fusion for Brain Tumor Monitoring
Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image.
OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images
Inspired by the complex relationships between OU and the high correlation between the (continuous) outcome labels (Spherical Equivalent and Axial Length), we propose a framework of copula-enhanced adapter convolutional neural network (CNN) learning with OU UWF fundus images (OUCopula) for joint prediction of multiple clinical scores.
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning
In response to this, inspired by the idea that the functions of the brain are shaped by its geometric structure, this paper integrates this idea into LoRA technology and proposes a new matrix transformation-based reparameterization method for efficient fine-tuning, named Matrix-Transformation based Low-Rank Adaptation (MTLoRA).
Natural Language Understanding
parameter-efficient fine-tuning
+1
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation
Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings.
Electrical Load Forecasting Model Using Hybrid LSTM Neural Networks with Online Correction
Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems.
A Unified Model for Active Battery Equalization Systems
Lithium-ion battery packs demand effective active equalization systems to enhance their usable capacity and lifetime.
Pyramid Feature Attention Network for Monocular Depth Prediction
Deep convolutional neural networks (DCNNs) have achieved great success in monocular depth estimation (MDE).
Brain-inspired and Self-based Artificial Intelligence
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum
Specifically, we construct a transfer curriculum over the source and intermediate domains based on Wasserstein distance, motivated by theoretical analysis of CDA.
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
In this paper, we introduce ChunkAttention, a prefix-aware self-attention module that can detect matching prompt prefixes across multiple requests and share their key/value tensors in memory at runtime to improve the memory utilization of KV cache.
Open Ad Hoc Teamwork with Cooperative Game Theory
Ad hoc teamwork poses a challenging problem, requiring the design of an agent to collaborate with teammates without prior coordination or joint training.
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions
Among existing methods for UTST tasks, attention masking approach and Large Language Models (LLMs) are deemed as two pioneering methods.
Flexible Physical Camouflage Generation Based on a Differential Approach
This involves incorporating a specially designed adversarial loss and covert constraint loss to guarantee the adversarial and covert nature of the camouflage in the physical world.
A Geometric Algorithm for Tubular Shape Reconstruction from Skeletal Representation
We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations.
Not All Weights Are Created Equal: Enhancing Energy Efficiency in On-Device Streaming Speech Recognition
This study delves into how weight parameters in speech recognition models influence the overall power consumption of these models.
FGeo-HyperGNet: Geometric Problem Solving Integrating Formal Symbolic System and Hypergraph Neural Network
The symbolic part is a formal system built on FormalGeo, which can automatically perform geomertic relational reasoning and algebraic calculations and organize the solving process into a solution hypertree with conditions as hypernodes and theorems as hyperedges.
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
Echoing the widely-reported "emergent abilities" of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences.
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks
Efficient parallel computing has become a pivotal element in advancing artificial intelligence.
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation
The previous methods either failed to accurately fit the face region or lost the interactive generative ability with other existing concepts in T2I models.
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation
A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed.
Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
This problem is even more severe in cell-free networks as there are many of these parameters to be acquired.
Localization of Dummy Data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach
Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization.
A New Method for Vehicle Logo Recognition Based on Swin Transformer
Additionally, the use of a transfer learning strategy enables our method to be on par with state-of-the-art VLR methods.
Label-free detection of exosomes from different cellular sources based on surface-enhanced Raman spectroscopy combined with machine learning models
In comparison to other machine learning analysis, our method used small amount of SERS data to allow a simple and rapid exosome detection, which enables a timely subsequent study of cell-cell interactions, communication mechanisms, and disease mechanisms in life sciences.
SCNet: Sparse Compression Network for Music Source Separation
We use a higher compression ratio on subbands with less information to improve the information density and focus on modeling subbands with more information.
Ranked #3 on
Music Source Separation
on MUSDB18-HQ
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.
GloTSFormer: Global Video Text Spotting Transformer
In this paper, we propose a novel Global Video Text Spotting Transformer GloTSFormer to model the tracking problem as global associations and utilize the Gaussian Wasserstein distance to guide the morphological correlation between frames.
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution.
PPBFL: A Privacy Protected Blockchain-based Federated Learning Model
Therefore, we propose A Privacy Protected Blockchain-based Federated Learning Model (PPBFL) to enhance the security of federated learning and encourage active participation of nodes in model training.
On Scaling Up a Multilingual Vision and Language Model
We explore the boundaries of scaling up a multilingual vision and language model both in terms of size of the components and the breadth of its training task mixture.
DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation
While ensemble models have proven effective, current research in the field of object detection typically focuses on the simple fusion of the outputs of all models, with limited attention being given to developing general adversarial patches that can function effectively in the physical world.
Semantic Draw Engineering for Text-to-Image Creation
Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models.
PGN: A perturbation generation network against deep reinforcement learning
Deep reinforcement learning has advanced greatly and applied in many areas.
Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting
Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, which is crucial to edit the incomplete utterance, and introduce words from irrelevant utterances.
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
To address this issue, we construct a \textbf{dual relation graph} where each node denotes a relation in the original KG (\textbf{primal entity graph}) and edges are constructed between relations sharing same head or tail entities.
H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer
In this work, we adopt an information theoretic perspective on it and propose a framework named H-ensemble, which dynamically learns the optimal linear combination, or ensemble, of source models for the target task, using a generalization of maximal correlation regression.
Hypergrah-Enhanced Dual Convolutional Network for Bundle Recommendation
While previous approaches have demonstrated notable performance, we argue that they may compromise the ternary relationship among users, items, and bundles.
Rich Human Feedback for Text-to-Image Generation
We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.
ALOHA: from Attention to Likes -- a unified mOdel for understanding HumAn responses to diverse visual content
Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior such as human attention and explicit, later-stage behavior such as subjective preferences/likes.
Learning a Low-Rank Feature Representation: Achieving Better Trade-Off between Stability and Plasticity in Continual Learning
This approach optimizes network parameters in the null space of the past tasks' feature representation matrix to guarantee the stability.
Explainable Trajectory Representation through Dictionary Learning
A hierarchical dictionary learning scheme is also proposed to ensure the algorithm's scalability on large networks, leading to a multi-scale trajectory representation.
Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling
Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism.
Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation
Road network extraction from satellite images is widely applicated in intelligent traffic management and autonomous driving fields.
Resource Allocation for Semantic Communication under Physical-layer Security
The secrecy rate is the communication rate at which no information is disclosed to an eavesdropper.
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data
To address this issue, we develop a novel personalized federated learning framework for heterogeneous data, which we refer to as FedSplit.
Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future
With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fostering the robust evolution of the industry ecosystem.
Deep Reinforcement Learning Based Optimal Energy Management of Multi-energy Microgrids with Uncertainties
To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper.
Perceptual Group Tokenizer: Building Perception with Iterative Grouping
In this paper, we propose the Perceptual Group Tokenizer, a model that entirely relies on grouping operations to extract visual features and perform self-supervised representation learning, where a series of grouping operations are used to iteratively hypothesize the context for pixels or superpixels to refine feature representations.
Ranked #24 on
Self-Supervised Image Classification
on ImageNet
Representation Learning
Self-Supervised Image Classification
+2
Advancing Attack-Resilient Scheduling of Integrated Energy Systems with Demand Response via Deep Reinforcement Learning
Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources.
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
Addressing this, we introduce \textsc{FinMem}, a novel LLM-based agent framework devised for financial decision-making.
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS).
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training
(2) The diverse semantics of side information that describes items and users from multi-level in a context different from recommendation systems.
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication
This article proposes Blockchain-based Federated Learning (FBChain) model for federated learning parameter communication to overcome the above two problems.
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts
This results in a set of candidate answers.
There is No Silver Bullet: Benchmarking Methods in Predictive Combinatorial Optimization
Our study shows that PnO approaches are better than PtO on 7 out of 8 benchmarks, but there is no silver bullet found for the specific design choices of PnO.
CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus images
The CeCNN formulates a multiresponse regression that relates multiple dependent discrete-continuous responses and the image covariate, where the nonlinearity of the association is modeled by a backbone CNN.
AdaptMVSNet: Efficient Multi-View Stereo with adaptive convolution and attention fusion
To this end, adaptive convolution is introduced to significantly improve the efficiency in speed and metrics compared to current methods.
In-Context Prompt Editing For Conditional Audio Generation
We show that the framework enhanced the audio quality across the set of collected user prompts, which were edited with reference to the training captions as exemplars.
FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving
In this paper, we have constructed a consistent formal plane geometry system.
R$^3$ Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context
More analyses and ablation studies show the robustness and generalization of R$^3$ prompting method in solving reasoning tasks in LLMs under noisy context.
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease
ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner.
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model
With the increasing importance of machine learning, the privacy and security of training data have become critical.
Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance
This work proposes a Transformer-based STVSA method to address this challenge.
Kernel Learning in Ridge Regression "Automatically" Yields Exact Low Rank Solution
Assuming that the covariates have nonzero explanatory power for the response only through a low dimensional subspace (central mean subspace), we find that the global minimizer of the finite sample kernel learning objective is also low rank with high probability.
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
A Zero-Shot Language Agent for Computer Control with Structured Reflection
To perform a task, recent works often require a model to learn from trace examples of the task via either supervised learning or few/many-shot prompting.
Language Models As Semantic Indexers
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
Automatic Macro Mining from Interaction Traces at Scale
Macros are building block tasks of our everyday smartphone activity (e. g., "login", or "booking a flight").
Secondary frequency control of islanded microgrid considering wind and solar stochastics
Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method.
Distribution-free risk assessment of regression-based machine learning algorithms
We solve the risk-assessment problem using the conformal prediction approach, which provides prediction intervals that are guaranteed to contain the true label with a given probability.
Low-carbon optimal dispatch of integrated energy system considering demand response under the tiered carbon trading mechanism
The example results show that considering the carbon trading cost and demand response under the tiered carbon trading mechanism, the total operating cost of IES is reduced by 5. 69% and the carbon emission is reduced by 17. 06%, which significantly improves the reliability, economy and low carbon performance of IES.
Explainable machine learning-based prediction model for diabetic nephropathy
We develop a predictive model by XGB algorithm to screen for DN.
A Demand-Supply Cooperative Responding Strategy in Power System with High Renewable Energy Penetration
In this context, unreasonable profit distributions on the demand-supply side would lead to the conflict of interests and diminish the effectiveness of cooperative responses.
Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
To instantiate this proposal, we design an adaptive rationale guidance network for fake news detection (ARG), in which SLMs selectively acquire insights on news analysis from the LLMs' rationales.
Learning Point-wise Abstaining Penalty for Point Cloud Anomaly Detection
We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance.
For A More Comprehensive Evaluation of 6DoF Object Pose Tracking
The limitations of previous scoring methods and error metrics are analyzed, based on which we introduce our improved evaluation methods.
Folding Attention: Memory and Power Optimization for On-Device Transformer-based Streaming Speech Recognition
Instead, the bottleneck lies in the linear projection layers of multi-head attention and feedforward networks, constituting a substantial portion of the model size and contributing significantly to computation, memory, and power usage.