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Found 14 papers, 6 papers with code
Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content.
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
The compensation stream is governed by a Dual-Activation Compensation (DAC) module.
AOCIL: Exemplar-free Analytic Online Class Incremental Learning with Low Time and Resource Consumption
Online Class Incremental Learning (OCIL) aims to train the model in a task-by-task manner, where data arrive in mini-batches at a time while previous data are not accessible.
G-ACIL: Analytic Learning for Exemplar-Free Generalized Class Incremental Learning
The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution, leading to intensified forgetting.
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
The DS-BPT pretrains model in streams of both supervised learning and self-supervised contrastive learning (SSCL) for base knowledge extraction.
Chimera: A Lossless Decoding Method for Accelerating Large Language Models Inference by Fusing all Tokens
Within this framework, we introduce a lightweight draft model that effectively utilizes previously generated tokens to predict subsequent words.
Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
In this work, we propose a novel method -- Convex-Concave Loss, which enables a high variance of training loss distribution by gradient descent.
ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference
We propose ConsistentEE, an early exiting method that is consistent in training and inference.
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task
In this paper, we approach the FSCIL by adopting analytic learning, a technique that converts network training into linear problems.
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks.
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection
Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively.
Analytic Learning of Convolutional Neural Network For Pattern Recognition
Training convolutional neural networks (CNNs) with back-propagation (BP) is time-consuming and resource-intensive particularly in view of the need to visit the dataset multiple times.
Accumulated Decoupled Learning: Mitigating Gradient Staleness in Inter-Layer Model Parallelization
Decoupled learning is a branch of model parallelism which parallelizes the training of a network by splitting it depth-wise into multiple modules.
Fully Decoupled Neural Network Learning Using Delayed Gradients
Training neural networks with back-propagation (BP) requires a sequential passing of activations and gradients, which forces the network modules to work in a synchronous fashion.
