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Found 26 papers, 8 papers with code
On the Relationship between Neural Machine Translation and Word Alignment
We find that although NMT models are difficult to capture word alignment for CFT words but these words do not sacrifice translation quality significantly, which provides an explanation why NMT is more successful for translation yet worse for word alignment compared to statistical machine translation.
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios.
PRIME: Protect Your Videos From Malicious Editing
With the development of generative models, the quality of generated content keeps increasing.
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness
However, these works lack analysis of adversarial information or perturbation, which cannot reveal the mystery of adversarial examples and lose proper interpretation.
Rethinking Adversarial Training with Neural Tangent Kernel
To the best of our knowledge, it is the first work leveraging the observations of kernel dynamics to improve existing AT methods.
Warfare:Breaking the Watermark Protection of AI-Generated Content
We propose Warfare, a unified methodology to achieve both attacks in a holistic way.
Alleviating the Effect of Data Imbalance on Adversarial Training
This framework consists of two components: (1) a new training strategy inspired by the effective number to guide the model to generate more balanced and informative AEs; (2) a carefully constructed penalty function to force a satisfactory feature space.
Omnipotent Adversarial Training in the Wild
We first introduce an oracle into the adversarial training process to help the model learn a correct data-label conditional distribution.
A Benchmark of Long-tailed Instance Segmentation with Noisy Labels
In this paper, we consider the instance segmentation task on a long-tailed dataset, which contains label noise, i. e., some of the annotations are incorrect.
Low-Light Hyperspectral Image Enhancement
The illumination enhancement branch is adopted to enlighten the low-frequency component with reduced resolution.
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks
Multiplication-less neural networks significantly reduce the time and energy cost on the hardware platform, as the compute-intensive multiplications are replaced with lightweight bit-shift operations.
Towards Robust Point Cloud Models with Context-Consistency Network and Adaptive Augmentation
Extensive evaluations indicate the integration of the two techniques provides much more robustness than existing defense solutions for 3D models.
MotionInput v2.0 supporting DirectX: A modular library of open-source gesture-based machine learning and computer vision methods for interacting and controlling existing software with a webcam
We also introduce a series of bespoke gesture recognition classifications as DirectInput triggers, including gestures for idle states, auto calibration, depth capture from a 2D RGB webcam stream and tracking of facial motions such as mouth motions, winking, and head direction with rotation.
Fingerprinting Generative Adversarial Networks
In this paper, we present the first fingerprinting scheme for the Intellectual Property (IP) protection of GANs.
Demystifying Learning of Unsupervised Neural Machine Translation
Unsupervised Neural Machine Translation or UNMT has received great attention in recent years.
SCNet: A Neural Network for Automated Side-Channel Attack
Information about system characteristics such as power consumption, electromagnetic leaks and sound can be exploited by the side-channel attack to compromise the system.
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder
Whereas adversarial training is employed as the main defence strategy against specific adversarial samples, it has limited generalization capability and incurs excessive time complexity.
Evaluating Explanation Methods for Neural Machine Translation
Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods.
Understanding Learning Dynamics for Neural Machine Translation
Despite the great success of NMT, there still remains a severe challenge: it is hard to interpret the internal dynamics during its training process.
Detecting and Understanding Generalization Barriers for Neural Machine Translation
Generalization to unseen instances is our eternal pursuit for all data-driven models.
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization
Many Data Augmentation (DA) methods have been proposed for neural machine translation.
On the Word Alignment from Neural Machine Translation
Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, this paper finds attention may almost fail to capture word alignment for some NMT models.
Understanding and Improving Hidden Representations for Neural Machine Translation
Multilayer architectures are currently the gold standard for large-scale neural machine translation.
Approximate Distribution Matching for Sequence-to-Sequence Learning
Then, we interpret sequence-to-sequence learning as learning a transductive model to transform the source local latent distributions to match their corresponding target distributions.
Generative Bridging Network for Neural Sequence Prediction
In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network).
Generative Bridging Network in Neural Sequence Prediction
In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network).
