Search Results for author:
Found 25 papers, 10 papers with code
Negative Label Guided OOD Detection with Pretrained Vision-Language Models
In this paper, we propose a novel post hoc OOD detection method, called NegLabel, which takes a vast number of negative labels from extensive corpus databases.
SEED: Customize Large Language Models with Sample-Efficient Adaptation for Code Generation
Specifically, SEED involves identifying error code generated by LLMs, employing Self-revise for code revision, optimizing the model with revised code, and iteratively adapting the process for continuous improvement.
Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models
CDD necessitates only the sampled texts to detect data contamination, by identifying the peakedness of LLM's output distribution.
Exact Tensor Completion Powered by Arbitrary Linear Transforms
In this work, a tensor completion problem is studied, which aims to perfectly recover the tensor from partial observations.
PACE: Improving Prompt with Actor-Critic Editing for Large Language Model
Large language models (LLMs) have showcased remarkable potential across various tasks by conditioning on prompts.
Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search
To overcome this challenge, in this paper, we propose knowledge condensation (KC), a simple yet effective knowledge distillation framework to boost the classification performance of the online FastText model under strict low latency constraints.
Detecting Out-of-distribution Data through In-distribution Class Prior
In this paper, we show that this assumption makes the above methods incapable when the ID model is trained with class-imbalanced data. Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where the OOD-to-ID probabilities should be the ID-class-prior distribution and propose two strategies to modify existing inference-time detection methods: 1) replace the uniform distribution with the ID-class-prior distribution if they explicitly use the uniform distribution; 2) otherwise, reweight their scores according to the similarity between the ID-class-prior distribution and the softmax outputs of the pre-trained model.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
On the Stability and Generalization of Triplet Learning
Triplet learning, i. e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e. g., face recognition and person re-identification.
Disentangled Feature Learning for Real-Time Neural Speech Coding
Recently end-to-end neural audio/speech coding has shown its great potential to outperform traditional signal analysis based audio codecs.
CodePAD: Sequence-based Code Generation with Pushdown Automaton
CodePAD can leverage existing sequence-based models, and we show that it can achieve 100\% grammatical correctness percentage on these benchmark datasets.
Wideband Channel Estimation for mmWave MIMO Systems with Beam Squint
With the scale of antenna arrays and the bandwidth increasing, many existing narrowband channel estimation methods ignoring the effect of beam squint may face severe performance degradation in wideband millimeter-wave (mmWave) communication systems.
Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation
To evaluate the effectiveness of our proposed loss, we implement and train an Antecedent Prioritized Tree-based code generation model called APT.
Latent-Domain Predictive Neural Speech Coding
Neural audio/speech coding has recently demonstrated its capability to deliver high quality at much lower bitrates than traditional methods.
Cross-Scale Vector Quantization for Scalable Neural Speech Coding
In this paper, we introduce a cross-scale scalable vector quantization scheme (CSVQ), in which multi-scale features are encoded progressively with stepwise feature fusion and refinement.
PyTSK: A Python Toolbox for TSK Fuzzy Systems
This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems.
MMRotate: A Rotated Object Detection Benchmark using PyTorch
We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning.
Cross-Image Relational Knowledge Distillation for Semantic Segmentation
Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples.
End-to-End Neural Speech Coding for Real-Time Communications
Deep-learning based methods have shown their advantages in audio coding over traditional ones but limited attention has been paid on real-time communications (RTC).
Self-supervised Contrastive Learning for EEG-based Sleep Staging
In detail, the network's performance depends on the choice of transformations and the amount of unlabeled data used in the training process of self-supervised learning.
TreeBERT: A Tree-Based Pre-Trained Model for Programming Language
In this paper, we present TreeBERT, a tree-based pre-trained model for improving programming language-oriented generation tasks.
MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging
To address this problem, we propose a new framework, called MRNet, for data-driven sleep staging by integrating a multi-scale feature fusion model and a Markov-based sequential correction algorithm.
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks
Test samples with the backdoor key will then be classified into the target class specified by the attacker.
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance.
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)
Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples.
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces
Learning a good substitute model is critical to the success of these attacks, but it requires a large number of queries to the target model.
