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Found 32 papers, 14 papers with code
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
By viewing ResNet as an explicit Euler discretization of an ordinary differential equation (ODE), for the first time, we find that the adversarial robustness of ResNet is connected to the numerical stability of the corresponding dynamic system.
Black-box Adversarial Attacks Against Image Quality Assessment Models
Specifically, we first formulate the attack problem as maximizing the deviation between the estimated quality scores of original and perturbed images, while restricting the perturbed image distortions for visual quality preservation.
EMBRE: Entity-aware Masking for Biomedical Relation Extraction
Information extraction techniques, including named entity recognition (NER) and relation extraction (RE), are crucial in many domains to support making sense of vast amounts of unstructured text data by identifying and connecting relevant information.
Mask Propagation for Efficient Video Semantic Segmentation
By reusing predictions from key frames, we circumvent the need to process a large volume of video frames individually with resource-intensive segmentors, alleviating temporal redundancy and significantly reducing computational costs.
Explaining How a Neural Network Play the Go Game and Let People Learn
The AI model has surpassed human players in the game of Go, and it is widely believed that the AI model has encoded new knowledge about the Go game beyond human players.
Data navigation on the ENCODE portal
Spanning two decades, the Encyclopaedia of DNA Elements (ENCODE) is a collaborative research project that aims to identify all the functional elements in the human and mouse genomes.
Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining
In MOTOR, we combine two kinds of basic medical knowledge, i. e., general and specific knowledge, in a complementary manner to boost the general pretraining process.
Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs
For faithfulness, we prove the uniqueness of the AND (OR) interaction in quantifying the effect of the AND (OR) relationship between input variables.
A Benchmark for Cycling Close Pass Near Miss Event Detection from Video Streams
Instance-level detection aims to detect which vehicle in the scene gives rise to a close pass near miss.
Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?
In this paper, we explain the inference logic of large language models (LLMs) as a set of symbolic concepts.
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation
To address the limitation, we propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning, named DCL.
Does a Neural Network Really Encode Symbolic Concepts?
Recently, a series of studies have tried to extract interactions between input variables modeled by a DNN and define such interactions as concepts encoded by the DNN.
CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
To further enhance forecasting accuracy, we introduce a memory-driven decoder.
Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition
Fault diagnosis is critical in many domains, as faults may lead to safety threats or economic losses.
Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation
To endow models with the capability of incorporating expert knowledge, we propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG), in which clinical relation triples are injected into the visual features as prior knowledge to drive the decoding procedure.
SymNMF-Net for The Symmetric NMF Problem
Furthermore, we analyze the constraints of the inversion layer to ensure the output stability of the network to a certain extent.
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
Based on the proposed metrics, we analyze two typical phenomena of the change of the transformation complexity during the training process, and explore the ceiling of a DNN's complexity.
Defining and Quantifying the Emergence of Sparse Concepts in DNNs
This paper aims to illustrate the concept-emerging phenomenon in a trained DNN.
Visualizing the Emergence of Intermediate Visual Patterns in DNNs
This paper proposes a method to visualize the discrimination power of intermediate-layer visual patterns encoded by a DNN.
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness
In this paper, we endeavor to design strategies to achieve universal adversarial robustness.
Generating Watermarked Adversarial Texts
Adversarial example generation has been a hot spot in recent years because it can cause deep neural networks (DNNs) to misclassify the generated adversarial examples, which reveals the vulnerability of DNNs, motivating us to find good solutions to improve the robustness of DNN models.
Optimization inspired Multi-Branch Equilibrium Models
Works have shown the strong connections between some implicit models and optimization problems.
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain knowledge for the generation of readable medical reports.
Understanding, Analyzing, and Optimizing the Complexity of Deep Models
Based on the proposed metrics, we analyze two typical phenomena of the change of the transformation complexity during the training process, and explore the ceiling of a DNN’s complexity.
Towards Understanding and Improving Dropout in Game Theory
Experimental results on various DNNs and datasets have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.
Disentanglement, Visualization and Analysis of Complex Features in DNNs
This paper aims to define, visualize, and analyze the feature complexity that is learned by a DNN.
Interpreting and Boosting Dropout from a Game-Theoretic View
This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions.
Interpreting and Disentangling Feature Components of Various Complexity from DNNs
This paper aims to define, quantify, and analyze the feature complexity that is learned by a DNN.
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation
Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure.
An Action Recognition network for specific target based on rMC and RPN
Meanwhile, the action recognition network is tested in our gesture and body posture data sets for specific target.
Impoved RPN for Single Targets Detection based on the Anchor Mask Net
Common target detection is usually based on single frame images, which is vulnerable to affected by the similar targets in the image and not applicable to video images.
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks
In this paper, we are interested in building lightweight and efficient convolutional neural networks.
