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
Relational Composition in Neural Networks: A Survey and Call to Action
Martin Wattenberg, Fernanda B. Viégas
On the Robustness of Fully-Spiking Neural Networks in Open-World Scenarios using Forward-Only Learning Algorithms
Erik B. Terres-Escudero, Javier Del Ser, Aitor Martínez-Seras, Pablo Garcia-Bringas
BERTer: The Efficient One
Pradyumna Saligram, Andrew Lanpouthakoun
The Effect of Training Schedules on Morphological Robustness and Generalization
Edoardo Barba, Anil Yaman, Giovanni Iacca
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset
Yi Sheng, Junhuan Yang, Jinyang Li, James Alaina, Xiaowei Xu, Yiyu Shi, Jingtong Hu, Weiwen Jiang, Lei Yang
Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligence
Shubha R. Kharel, Prashansa Mukim, Piotr Maj, Grzegorz W. Deptuch, Shinjae Yoo, Yihui Ren, Soumyajit Mandal
Neuromorphic Circuit Simulation with Memristors: Design and Evaluation Using MemTorch for MNIST and CIFAR
Julio Souto, Guillermo Botella, Daniel García, Raúl Murillo, Alberto del Barrio
Configural processing as an optimized strategy for robust object recognition in neural networks
Hojin Jang, Pawan Sinha, Xavier Boix
Long Input Sequence Network for Long Time Series Forecasting
Chao Ma, Yikai Hou, Xiang Li, Yinggang Sun, Haining Yu
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos
Sabah Abdulazeez Jebur, Khalid A. Hussein, Haider Kadhim Hoomod, Laith Alzubaidi, Ahmed Ali Saihood, YuanTong Gu
On Diversity in Discriminative Neural Networks
Brahim Oubaha, Claude Berrou, Xueyao Ji, Yehya Nasser, Raphaël Le Bidan
A Survey on Universal Approximation Theorems
Midhun T Augustine
Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild Dataset
Mijoo Kim, Junseok Kwon