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Found 12 papers, 0 papers with code
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters.
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation
Specifically, the self-supervised learning-based augmenter can automatically delete noisy items from sequences and insert new items that better capture item transition patterns, generating a higher-quality augmented sequence.
Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model
To tackle this issue, we propose a model that can efficiently learn edge features for GCL, namely AugmentationFree Edge Contrastive Learning (AFECL) to achieve edgeedge contrast.
Dynamics of Adaptive Continuous Attractor Neural Networks
The attractor property empowers a neural system to encode information robustly, but it also incurs the difficulty of rapid update of network states, which can impair information update and search in the brain.
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape
Fine-tuning large-scale pre-trained models is prohibitively expensive in terms of computational and memory costs.
Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implications
As AIGC has impacted our society profoundly in the past years, ethical issues have received tremendous attention.
Privacy-Preserving Sequential Recommendation with Collaborative Confusion
Unlike existing research, we capture collaborative signals of neighbor interaction sequences and directly inject indistinguishable items into the target sequence before the recommendation process begins, thereby increasing the perplexity of the target sequence.
Learning to Prove Trigonometric Identities
We define the normalized form of trigonometric identities, design a set of rules for the proof and put forward a method which can generate theoretically infinite trigonometric identities.
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network
GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality.
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization
In this paper, we propose a novel approach for minimax optimization, called Minimax Cubic Newton (MCN), which could find an $\big(\varepsilon,\kappa^{1. 5}\sqrt{\rho\varepsilon}\,\big)$-second-order stationary point of $P({\bf x})$ with calling ${\mathcal O}\big(\kappa^{1. 5}\sqrt{\rho}\varepsilon^{-1. 5}\big)$ times of second-order oracles and $\tilde{\mathcal O}\big(\kappa^{2}\sqrt{\rho}\varepsilon^{-1. 5}\big)$ times of first-order oracles, where $\kappa$ is the condition number and $\rho$ is the Lipschitz continuous constant for the Hessian of $f({\bf x},{\bf y})$.
Optimal Designs of Gaussian Processes with Budgets for Hyperparameter Optimization
Moreover, comparisons between different initial designs with the same model show the advantage of the proposed optimal design.
Accelerated Value Iteration via Anderson Mixing
A2VI is more efficient than the modified policy iteration, which is a classical approximate method for policy evaluation.
