Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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Data Representation and Clustering with Double Low-Rank Constraintsure learning method, uses low rank constraints to extract the low-rank subspace structure of high-dimensional data. However, LRR is highly dependent on the multi-subspace property of the data itself, which is easily disturbed by some higher intensity global noise. Thus, a data representation learnin

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RoMA: A Method for Neural Network Robustness Measurement and AssessmentHowever, their reliability is heavily plagued by .: inputs generated by adding tiny perturbations to correctly-classified inputs, and for which the neural network produces erroneous results. In this paper, we present a new method called . (.), which measures the robustness of a neural network model

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O,GPT: A Guidance-Oriented Periodic Testing Framework with Online Learning, Online Testing, and Onli most previous PTs follow an inflexible offline-policy method, which can hardly adjust testing procedure using the online feedback instantly. In this paper, we develop a dynamic and executed online periodic testing framework called O.GPT, which selects the most suitable questions step by step, depen

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Temporal-Sequential Learning with Columnar-Structured Spiking Neural Networksowever, most of the existing sequential memory models can only handle sequences that lack temporal information between elements, such as sentences. In this paper, we propose a columnar-structured model that can memorize sequences with variable time intervals. Each column is composed of several spiki

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Towards a Unified Benchmark for Reinforcement Learning in Sparse Reward Environmentsosed every year. Despite promising results demonstrated in various sparse reward environments, this domain lacks a unified definition of a sparse reward environment and an experimentally fair way to compare existing algorithms. These issues significantly affect the in-depth analysis of the underlyin