arXiv:2603.10095v1 Announce Type: new Abstract: Time-series forecasting often faces challenges from non-stationarity, particularly distributional drift, where the data distribution evolves over time. This dynamic behavior can undermine the effectiveness of adaptive optimizers, such as Adam, which are typically designed for…
arXiv:2603.10485v1 Announce Type: cross Abstract: In this work we study the convergence properties of the Dual Space Preconditioned Gradient Descent, encompassing optimizers such as Normalized Gradient Descent, Gradient Clipping and Adam. We consider preconditioners of the form $nabla K$, where…
arXiv:2603.10099v1 Announce Type: new Abstract: The US Census Bureau Disclosure Avoidance System (DAS) balances confidentiality and utility requirements for the decennial US Census (Abowd et al., 2022). The DAS was used in the 2020 Census to produce demographic datasets critically…
arXiv:2603.10599v1 Announce Type: cross Abstract: We present a JAX implementation of the Self-Scaled Broyden family of quasi-Newton methods, fully compatible with JAX and building on the Optimistix~cite{rader_optimistix_2024} optimisation library. The implementation includes BFGS, DFP, Broyden and their Self-Scaled variants(SSBFGS, SSDFP,…
arXiv:2603.10100v1 Announce Type: new Abstract: Modern CNNs’ high computational demands hinder edge deployment, as traditional “hard” sparsity (skipping mathematical zeros) loses effectiveness in deep layers or with smooth activations like Tanh. We propose a “soft sparsity” paradigm using a hardware…
arXiv:2603.10753v1 Announce Type: cross Abstract: More and more companies’ Intellectual Property (IP) is being integrated into Neural Network (NN) models. This IP has considerable value for companies and, therefore, requires adequate protection. For example, an attacker might replicate a production…
arXiv:2603.10101v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capacity of Large Language Models (LLMs). However, RLVR solely relies on final answers as outcome rewards, neglecting the correctness of intermediate reasoning steps. Training…
arXiv:2603.11042v1 Announce Type: cross Abstract: Generating music that temporally aligns with video events is challenging for existing text-to-music models, which lack fine-grained temporal control. We introduce V2M-Zero, a zero-pair video-to-music generation approach that outputs time-aligned music for video. Our method…
arXiv:2603.10123v1 Announce Type: new Abstract: The “Lost in the Middle” phenomenon — a U-shaped performance curve where LLMs retrieve well from the beginning and end of a context but fail in the middle — is widely attributed to learned Softmax…
arXiv:2410.02113v3 Announce Type: replace Abstract: Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have emerged as the preferred architecture for PDEs due to their ability to…
