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Found 14 papers, 6 papers with code
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis
SILTN is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language (FOL).
EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection
To address these issues, we propose an encoder-decoder data augmentation (EDDA) framework.
Speech Translation with Large Language Models: An Industrial Practice
Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM.
RepCodec: A Speech Representation Codec for Speech Tokenization
To improve the performance of these discrete speech tokens, we present RepCodec, a novel speech representation codec for semantic speech tokenization.
Fast Adversarial Training with Adaptive Step Size
While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it hard to scale to large datasets like ImageNet.
Black-box Prompt Learning for Pre-trained Language Models
Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training
This lets us show that the decay in generalization performance of adversarial training is a result of the model's attempt to fit hard adversarial instances.
Improving Adversarial Defense with Self-supervised Test-time Fine-tuning
Although adversarial training and its variants currently constitute the most effective way to achieve robustness against adversarial attacks, their poor generalization limits their performance on the test samples.
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling
We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs.
CorrAttack: Black-box Adversarial Attack with Structured Search
We show that searching over the structured space can be approximated by a time-varying contextual bandits problem, where the attacker takes feature of the associated arm to make modifications of the input, and receives an immediate reward as the reduction of the loss function.
Prototype Completion with Primitive Knowledge for Few-Shot Learning
To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples.
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
We consider nonconvex-concave minimax optimization problems of the form $\min_{\bf x}\max_{\bf y\in{\mathcal Y}} f({\bf x},{\bf y})$, where $f$ is strongly-concave in $\bf y$ but possibly nonconvex in $\bf x$ and ${\mathcal Y}$ is a convex and compact set.
Black-Box Adversarial Attack with Transferable Model-based Embedding
We present a new method for black-box adversarial attack.
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack
Zeroth-order optimization is an important research topic in machine learning.
