Neural Network
Neural networks are computational models inspired by the structure and function of the brain, primarily aimed at approximating complex functions and solving diverse problems through learning from data. Current research emphasizes improving efficiency and robustness, exploring novel architectures like sinusoidal neural fields and hybrid models combining neural networks with radial basis functions, as well as developing methods for understanding and manipulating the internal representations learned by these networks, such as through hyper-representations of network weights. These advancements are driving progress in various fields, including computer vision, natural language processing, and scientific modeling, by enabling more accurate, efficient, and interpretable AI systems.
Papers
Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural Network
Irfan Nafiz Shahan, Pulok Ahmed Auvi
Comparative Study of Neural Network Methods for Solving Topological Solitons
Koji Hashimoto, Koshiro Matsuo, Masaki Murata, Gakuto Ogiwara
Proportional infinite-width infinite-depth limit for deep linear neural networks
Federico Bassetti, Lucia Ladelli, Pietro Rotondo
Analytic Continuation by Feature Learning
Zhe Zhao, Jingping Xu, Ce Wang, Yaping Yang
Memory Backdoor Attacks on Neural Networks
Eden Luzon, Guy Amit, Roy Weiss, Yisroel Mirsky
Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network
Yuxuan Gu, Catherine Spurin, Gege Wen
Creating a Formally Verified Neural Network for Autonomous Navigation: An Experience Report
Syed Ali Asadullah Bukhari, Thomas Flinkow, Medet Inkarbekov, Barak A. Pearlmutter, Rosemary Monahan
Delta-NAS: Difference of Architecture Encoding for Predictor-based Evolutionary Neural Architecture Search
Arjun Sridhar, Yiran Chen
On Generalization Bounds for Neural Networks with Low Rank Layers
Andrea Pinto, Akshay Rangamani, Tomaso Poggio
Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm
Rushabh Solanki, Elliot Creager
An Evolutional Neural Network Framework for Classification of Microarray Data
Maryam Eshraghi Evari, Md Nasir Sulaiman, Amir Rajabi Behjat
Energy-based features and bi-LSTM neural network for EEG-based music and voice classification
Isaac Ariza, Ana M. Barbancho, Lorenzo J. Tardon, Isabel Barbancho
Trojan Cleansing with Neural Collapse
Xihe Gu, Greg Fields, Yaman Jandali, Tara Javidi, Farinaz Koushanfar
Tensor-Based Foundations of Ordinary Least Squares and Neural Network Regression Models
Roberto Dias Algarte
A new Input Convex Neural Network with application to options pricing
Vincent Lemaire, Gilles Pagès, Christian Yeo
Exploring the Manifold of Neural Networks Using Diffusion Geometry
Elliott Abel, Andrew J. Steindl, Selma Mazioud, Ellie Schueler, Folu Ogundipe, Ellen Zhang, Yvan Grinspan, Kristof Reimann, Peyton Crevasse, Dhananjay Bhaskar, Siddharth Viswanath, Yanlei Zhang, Tim G. J. Rudner, Ian Adelstein, Smita Krishnaswamy
Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment
Ekaterina Shumitskaya, Mikhail Pautov, Dmitriy Vatolin, Anastasia Antsiferova
The Hermeneutic Turn of AI: Is the Machine Capable of Interpreting?
Remy Demichelis
SNN-Based Online Learning of Concepts and Action Laws in an Open World
Christel Grimaud (IRIT-LILaC), Dominique Longin (IRIT-LILaC), Andreas Herzig (IRIT-LILaC)
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning
Hiroshi Sato, Masashi Konosu, Sho Sakaino, Toshiaki Tsuji