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Found 44 papers, 9 papers with code
VVRec: Reconstruction Attacks on DL-based Volumetric Video Upstreaming via Latent Diffusion Model with Gamma Distribution
We evaluate VVRec using three volumetric video datasets.
Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation
Building on this concept, we introduce a series-symbol (S2) dual-modulity data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic representations.
Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein
Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data.
Enabling Time-series Foundation Model for Building Energy Forecasting via Contrastive Curriculum Learning
Advances in time-series forecasting are driving a shift from conventional machine learning models to foundation models (FMs) that are trained with generalized knowledge.
PolyModel for Hedge Funds' Portfolio Construction Using Machine Learning
This study explores the enhancement of hedge fund investment performance through the integration of machine learning techniques, the application of PolyModel feature selection, and the analysis of fund size.
Reference Signal-Based Waveform Design for Integrated Sensing and Communications System
Then, a weighted sum CRB minimization problem on the distance and velocity estimations is formulated by considering communication rate requirement and RS intervals constraints, which is a mixed-integer problem due to the discrete RS interval values.
MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation
However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes.
SAMEdge: An Edge-cloud Video Analytics Architecture for the Segment Anything Model
SAMEdge integrates new modules on the edge and the cloud to maximize analytics accuracy under visual prompts and image prompts input with latency constraints.
Alt-MoE:A Scalable Framework for Bidirectional Multimodal Alignment and Efficient Knowledge Integration
By utilizing a sequential alternating one-way alignment strategy, Alt-MoE iteratively refines the model to achieve bidirectional alignment.
Receiver Selection and Transmit Beamforming for Multi-static Integrated Sensing and Communications
Next-generation wireless networks are expected to develop a novel paradigm of integrated sensing and communications (ISAC) to enable both the high-accuracy sensing and high-speed communications.
AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction
This confirms the value of adversarial training in reducing stochasticity and bias for stock volatility prediction tasks.
Coarse-To-Fine Tensor Trains for Compact Visual Representations
This has prevented practitioners from deploying the full potential of tensor networks for visual data.
A Survey on Visual Mamba
This encompasses general visual tasks, Medical visual tasks (e. g., 2D / 3D segmentation, classification, and image registration, etc.
Rethinking Few-shot 3D Point Cloud Semantic Segmentation
The former arises from non-uniform point sampling, allowing models to distinguish the density disparities between foreground and background for easier segmentation.
Few-shot 3D Point Cloud Semantic Segmentation
Segmentation
+1
Changes-Aware Transformer: Learning Generalized Changes Representation
After refinement, the changed pixels in the difference feature space are closer to each other, which facilitates change detection.
Gain and Phase: Decentralized Stability Conditions for Power Electronics-Dominated Power Systems
This paper proposes decentralized stability conditions for multi-converter systems based on the combination of the small gain theorem and the small phase theorem.
Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs
To distinguish the hard pixels from the source images, we achieve the determination of hard pixels by considering the inconsistency among the detection results of focus areas in source images.
Dual-Stream Diffusion Net for Text-to-Video Generation
In particular, the designed two diffusion streams, video content and motion branches, could not only run separately in their private spaces for producing personalized video variations as well as content, but also be well-aligned between the content and motion domains through leveraging our designed cross-transformer interaction module, which would benefit the smoothness of generated videos.
Privacy Protectability: An Information-theoretical Approach
We can also evaluate a privacy protection scheme, e. g., assume it obfuscates the video data, what level of protection this scheme has achieved after obfuscation.
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection
3D object detection is a critical task in autonomous driving.
Synchronization of Diverse Agents via Phase Analysis
They can also model the controllers of the agents which may be different for each agent or uniform for all the agents.
Identifying Operation Equilibrium in Integrated Electricity, Natural Gas, and Carbon-Emission Markets
Carbon emission markets can play a significant role in this transition by putting a price on carbon and giving electricity producers an incentive to reduce their emissions.
Deep Decarbonization of Multi-Energy Systems: A Carbon-Oriented Framework with Cross Disciplinary Technologies
The retirement of unabated coal power plants, the plummeting cost of renewable energy technologies, along with more aggressive public policies and regulatory reforms, are occurring at an unprecedented speed to decarbonize the power and energy systems towards the 2030 and 2050 climate goals.
Query-Efficient Adversarial Attack Based on Latin Hypercube Sampling
However, existing BA methods craft adversarial examples by leveraging a simple random sampling (SRS) to estimate the gradient, consuming a large number of model queries.
Generalizable Neural Radiance Fields for Novel View Synthesis with Transformer
We propose a Transformer-based NeRF (TransNeRF) to learn a generic neural radiance field conditioned on observed-view images for the novel view synthesis task.
An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration
Conversion rate (CVR) prediction is becoming in- creasingly important in the multi-billion dollar on- line display advertising industry.
A Sparsity Algorithm with Applications to Corporate Credit Rating
We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings.
On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation
We then show that task allocation with task importance for MTL (TATIM) is a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts.
A Phase Theory of MIMO LTI Systems
In this paper, we define the phase response for a class of multi-input multi-output (MIMO) linear time-invariant (LTI) systems whose frequency responses are (semi-)sectorial at all frequencies.
Multi-view 3D Reconstruction with Transformer
Unlike previous CNN-based methods using a separate design, we unify the feature extraction and view fusion in a single Transformer network.
Ranked #7 on
3D Reconstruction
on ShapeNet
Multi-View 3D Reconstruction With Transformers
Unlike previous CNN-based methods using a separate design, we unify the feature extraction and view fusion in a single Transformer network.
Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance
We find that using imaging techniques to input data for CNN works better for financial ratio data but is not significantly better than simply using the 1D input directly for fundamental data.
Application of Deep Neural Networks to assess corporate Credit Rating
The goal of the analysis is to improve application of machine learning algorithms to credit assessment.
CHAIN: Concept-harmonized Hierarchical Inference Interpretation of Deep Convolutional Neural Networks
For net-decisions being interpreted, the proposed method presents the CHAIN interpretation in which the net decision can be hierarchically deduced into visual concepts from high to low semantic levels.
Distributed Machine Learning through Heterogeneous Edge Systems
Many emerging AI applications request distributed machine learning (ML) among edge systems (e. g., IoT devices and PCs at the edge of the Internet), where data cannot be uploaded to a central venue for model training, due to their large volumes and/or security/privacy concerns.
Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling
Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects.
W-RNN: News text classification based on a Weighted RNN
For the problem that the feature high dimensionality and unclear semantic relationship in text data representation, we first utilize the word vector to represent the vocabulary in the text and use Recurrent Neural Network (RNN) to extract features of the serialized text data.
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Existing methods for single network learning cannot solve this problem due to the domain shift across networks.
Adversarial Training Methods for Network Embedding
To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain.
Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state
We study $e^{+}e^{-}$ collisions with a $\pi^{+}\pi^{-}\pi^{0}\eta_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4. 226$, $4. 258$, $4. 358$, $4. 416$, and $4. 600$ GeV.
High Energy Physics - Experiment
CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks
In this work, we propose a CHannel-wise disentangled InterPretation (CHIP) model to give the visual interpretation to the predictions of DCNNs.
SPI-Optimizer: an integral-Separated PI Controller for Stochastic Optimization
Inspired by the conditional integration idea in classical control society, we propose SPI-Optimizer, an integral-Separated PI controller based optimizer WITHOUT introducing extra hyperparameter.
Adversarial Network Embedding
Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization.
Supervised Deep Hashing for Hierarchical Labeled Data
Specifically, we define a novel similarity formula for hierarchical labeled data by weighting each layer, and design a deep convolutional neural network to obtain a hash code for each data point.
