Deep Learning
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization Haitz Sáez de Ocáriz Borde Oxford Robotics Institute University of Oxford Álvaro Arroyo
Recent research indicates that the performance of machine learning models can be improved by aligning the geometry of the latent space with the underlying data structure.
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Igor Colin, Ludovic DOS SANTOS, Kevin Scaman
Neural Information Processing Systems http://nips.cc/
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
Hajin Shim, Sung Ju Hwang, Eunho Yang
Neural Information Processing Systems http://nips.cc/
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems Bin Lei
Recent advancements in large language models, such as GPT -4, have demonstrated remarkable capabilities in processing standard queries.
Variational Memory Encoder-Decoder
Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh
Neural Information Processing Systems http://nips.cc/
Binarized Spectral Compressive Imaging
Consequently, these methods can hardly be deployed on resource-limited mobile devices.
Generative Neural Machine Translation
Neural Information Processing Systems http://nips.cc/
Deep Generative Video Compression
Salvator Lombardo, JUN HAN, Christopher Schroers, Stephan Mandt
Our approach builds upon variational autoencoder (V AE) models for sequential data and combines them with recent work on neural image compression.
H3T: Efficient Integration of Memory Optimization and Parallelism for High-Throughput Transformer Training Y uzhong Wang
Transformer-based models, their huge parameter size poses a serious challenge to their training, both from the storage and computation perspectives.