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Found 41 papers, 13 papers with code
In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor
Graphene based nanomaterials have attracted significant attention for their potentials in biomedical and biotechnology applications in recent years, owing to the outstanding physical and chemical properties.
Sculpting Molecules in 3D: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies.
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.
Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning
The Dataset Aggregation, or DAgger approach to this problem simply collects more data to cover these failure states.
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning
Specifically, in terms of share transfer, Chu-ko-nu breaks the probability P barrier by supplementing a redistribution process of secret key components (the sum of all components is the secret key), thus ensuring the reusability of the secret key.
Enhancing Temporal Knowledge Graph Forecasting with Large Language Models via Chain-of-History Reasoning
Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based on given histories.
Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One
Building on this insight and observation, we develop FairThinking, a pipeline designed to automatically generate roles that enable LLMs to articulate diverse perspectives for fair expressions.
Multi-target Detection for Reconfigurable Holographic Surfaces Enabled Radar
Multi-target detection is one of the primary tasks in radar-based localization and sensing, typically built on phased array antennas.
Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing
Face anti-spoofing is crucial for ensuring the security and reliability of face recognition systems.
DREAM: Debugging and Repairing AutoML Pipelines
In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and hyperparameters for a given task.
Automatic Implementation of Neural Networks through Reaction Networks -- Part I: Circuit Design and Convergence Analysis
Information processing relying on biochemical interactions in the cellular environment is essential for biological organisms.
Skywork: A More Open Bilingual Foundation Model
In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3. 2 trillion tokens drawn from both English and Chinese texts.
Push Past Green: Learning to Look Behind Plant Foliage by Moving It
We use self-supervision to train SRPNet, a neural network that predicts what space is revealed on execution of a candidate action on a given plant.
SIFTER: A Task-specific Alignment Strategy for Enhancing Sentence Embeddings
This indicates that different downstream tasks have different levels of sensitivity to sentence components.
A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions
In this work, we propose a novel peer-to-peer federated continual learning strategy to improve low-dose CT imaging performance from multiple institutions.
Pre-trained transformer for adversarial purification
It is frightening that deep neural networks are vulnerable and sensitive to adversarial attacks, the most common one of which for the services is evasion-based.
SimHaze: game engine simulated data for real-world dehazing
Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models.
B2Opt: Learning to Optimize Black-box Optimization with Little Budget
To achieve this, 1) drawing on the mechanism of genetic algorithm, we propose a deep neural network framework called B2Opt, which has a stronger representation of optimization strategies based on survival of the fittest; 2) B2Opt can utilize the cheap surrogate functions of the target task to guide the design of the efficient optimization strategies.
Efficient Map Sparsification Based on 2D and 3D Discretized Grids
Furthermore, to reduce the influence of different spatial distributions between the mapping and query sequences, which is not considered in previous methods, we also introduce a space constraint term based on 3D discretized grids.
MUter: Machine Unlearning on Adversarially Trained Models
However, existing methods focus exclusively on unlearning from standard training models and do not apply to adversarial training models (ATMs) despite their popularity as effective defenses against adversarial examples.
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer
When receiving the unlearning requests, we transfer the knowledge of the non-target data points from the original model to the unlearned model and meanwhile prohibit the knowledge of the target data points via our proposed knowledge transfer and prohibition (KTP) method.
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective
The experimental evaluations manifest the superiority of SAT over other state-of-the-art AT mechanisms in defending against adversarial attacks against both output and intermediate layers.
Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation
We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context.
rPPG-Toolbox: Deep Remote PPG Toolbox
Camera-based physiological measurement is a fast growing field of computer vision.
MobilePhys: Personalized Mobile Camera-Based Contactless Physiological Sensing
Camera-based contactless photoplethysmography refers to a set of popular techniques for contactless physiological measurement.
OmiTrans: generative adversarial networks based omics-to-omics translation framework
With the rapid development of high-throughput experimental technologies, different types of omics (e. g., genomics, epigenomics, transcriptomics, proteomics, and metabolomics) data can be produced from clinical samples.
Consensus-Based Decentralized Energy Trading for Distributed Energy Resources
In smart grids, distributed energy resources (DERs) have penetrated residential zones to provide a new form of electricity supply, mainly from renewable energy.
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks
The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications.
SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints
Object SLAM introduces the concept of objects into Simultaneous Localization and Mapping (SLAM) and helps understand indoor scenes for mobile robots and object-level interactive applications.
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning
Researchers are increasingly focusing on intelligent games as a hot research area. The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined their effect on wargaming, it solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training. At the same time, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, and obtained data on red and blue conflict. Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations.
Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection
Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder.
Perception-and-Regulation Network for Salient Object Detection
Effective fusion of different types of features is the key to salient object detection.
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data
To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE.
GraphFM: Graph Factorization Machines for Feature Interaction Modeling
To solve the problems, we propose a novel approach, Graph Factorization Machine (GraphFM), by naturally representing features in the graph structure.
Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey
This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings.
OmiEmbed: a unified multi-task deep learning framework for multi-omics data
To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules.
Flow-Sensory Contact Electrification of Graphene
All-electronic interrogation of biofluid flow velocity by sensors incorporated in ultra-low-power or self-sustained systems offers the promise of enabling multifarious emerging research and applications.
Applied Physics
Heterogeneous Graph Collaborative Filtering
In this work, we propose to model user-item interactions as a heterogeneous graph which consists of not only user-item edges indicating their interaction but also user-user edges indicating their similarity.
Unsupervised Visual Representation Learning with Increasing Object Shape Bias
(Very early draft)Traditional supervised learning keeps pushing convolution neural network(CNN) achieving state-of-art performance.
