Publications related to Prior art

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

Bayes Security: A Not So Average Metric

Carmela González Troncoso, Giovanni Cherubin

Security system designers favor worst-case security metrics, such as those derived from differential privacy (DP), due to the strong guarantees they provide. On the downside, these guarantees result in a high penalty on the system's performance. In this pa ...

IEEE2023

Are socially-aware trajectory prediction models really socially-aware?

Alexandre Massoud Alahi, Seyed Mohsen Moosavi Dezfooli, Saeed Saadatnejad, Mohammadhossein Bahari, Pedram Khorsandi

Our transportation field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigation or pedestrian flow simulations, their adversarial robustne ...

2022

Modeling Object Dissimilarity for Deep Saliency Prediction

Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Bahar Aydemir, Seungryong Kim, Deblina Bhattacharjee

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects. Despit ...

2022

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

Zhen Wei, Zhiye Wang, Peixia Li

Person retrieval aims at effectively matching the pedestrian images over an extensive database given a specified identity. As extracting effective features is crucial in a high-performance retrieval system, recent significant progress was achieved by part- ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation

François Fleuret, Camilla Carta

We present DepthInSpace, a self-supervised deep-learning method for depth estimation using a structured-light camera. The design of this method is motivated by the commercial use case of embedded depth sensors in nowadays smartphones. We first propose to u ...

IEEE2021

Seamless integration of design and Kirchhoff-Love shell analysis using analysis-suitable unstructured T-splines

Xiaodong Wei, Deepesh Toshniwal

Analysis-suitable T-splines (ASTS) including both extraordinary points and T-junctions are used to solve Kirchhoff-Love shell problems. Extraordinary points are required to represent surfaces with arbitrary topological genus. T-junctions enable local refin ...

ELSEVIER SCIENCE SA2020

Crowding reveals fundamental differences in local vs. global processing in humans and machines

Michael Herzog, Aurélien Bornet, Adrien Christophe Doerig, Oh-Hyeon Choung

Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have suggested t ...

2020

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Prior art

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Economic Effect of Patents and the Patent System: Insights from Linked Patent-Product Data

Bayes Security: A Not So Average Metric

Are socially-aware trajectory prediction models really socially-aware?

Modeling Object Dissimilarity for Deep Saliency Prediction

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation

Seamless integration of design and Kirchhoff-Love shell analysis using analysis-suitable unstructured T-splines

Crowding reveals fundamental differences in local vs. global processing in humans and machines

Graph Chatbot

Chat with Graph Search

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Bayes Security: A Not So Average Metric

Seamless integration of design and Kirchhoff-Love shell analysis using analysis-suitable unstructured T-splines

Modeling Object Dissimilarity for Deep Saliency Prediction

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation

Economic Effect of Patents and the Patent System: Insights from Linked Patent-Product Data

Crowding reveals fundamental differences in local vs. global processing in humans and machines

Are socially-aware trajectory prediction models really socially-aware?