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Found 17 papers, 6 papers with code
DualView: Data Attribution from the Dual Perspective
In this work we present DualView, a novel method for post-hoc data attribution based on surrogate modelling, demonstrating both high computational efficiency, as well as good evaluation results.
AttnLRP: Attention-Aware Layer-wise Relevance Propagation for Transformers
Large Language Models are prone to biased predictions and hallucinations, underlining the paramount importance of understanding their model-internal reasoning process.
Layer-wise Feedback Propagation
In this paper, we present Layer-wise Feedback Propagation (LFP), a novel training approach for neural-network-like predictors that utilizes explainability, specifically Layer-wise Relevance Propagation(LRP), to assign rewards to individual connections based on their respective contributions to solving a given task.
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations
Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input level typically resemble the models' predicted segmentation mask or bounding box.
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation
In this work we introduce the Concept Relevance Propagation (CRP) approach, which combines the local and global perspectives and thus allows answering both the "where" and "what" questions for individual predictions.
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable concepts in the latent space.
Curiously Effective Features for Image Quality Prediction
The performance of visual quality prediction models is commonly assumed to be closely tied to their ability to capture perceptually relevant image aspects.
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons
With the growing demand for deploying deep learning models to the "edge", it is paramount to develop techniques that allow to execute state-of-the-art models within very tight and limited resource constraints.
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic.
Trends and Advancements in Deep Neural Network Communication
Due to their great performance and scalability properties neural networks have become ubiquitous building blocks of many applications.
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for little loss of information.
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks.
Focus Group on Artificial Intelligence for Health
Artificial Intelligence (AI) - the phenomenon of machines being able to solve problems that require human intelligence - has in the past decade seen an enormous rise of interest due to significant advances in effectiveness and use.
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks.
Explainable artificial intelligence
General Classification
+2
The Convergence of Machine Learning and Communications
The areas of machine learning and communication technology are converging.
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
We present a deep neural network-based approach to image quality assessment (IQA).
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment
In most practical situations, the compression or transmission of images and videos creates distortions that will eventually be perceived by a human observer.
Ranked #12 on
Video Quality Assessment
on MSU FR VQA Database
