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Found 28 papers, 8 papers with code
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Deep semi-supervised learning (SSL) has been shown very effectively.
Micro Text Classification Based on Balanced Positive-Unlabeled Learning
In real-world text classification tasks, negative texts often contain a minimal proportion of negative content, which is especially problematic in areas like text quality control, legal risk screening, and sensitive information interception.
D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning
In this paper, we first establish data selection criteria based on three distinct aspects of data value: diversity, difficulty, and dependability, and then propose the D3 method comprising two key steps of scoring and selection.
LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM
To address these limitations, we study data generation for legal reasoning to improve the legal reasoning performance of open-source LLMs with the help of proprietary LLMs.
Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models
The integration of slow-thinking mechanisms into large language models (LLMs) offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified by systems like OpenAI's o1.
Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning
In this paper, we present the first theoretical error decomposition analysis of these techniques, breaking down their error into estimation error and model error.
TabFSBench: Tabular Benchmark for Feature Shifts in Open Environment
TabFSBench evaluates impacts of four distinct feature-shift scenarios on four tabular model categories across various datasets and assesses the performance of large language models (LLMs) and tabular LLMs in the tabular benchmark for the first time.
Contrast-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment
Vision-language models (VLMs), such as CLIP, have demonstrated exceptional generalization capabilities and can quickly adapt to downstream tasks through prompt fine-tuning.
Vision-Language Model Selection and Reuse for Downstream Adaptation
The proposal is highly computationally efficient and growable since the model labeling process is completed target task independent and the ability could grow with the number of candidate VLMs.
CGI: Identifying Conditional Generative Models with Example Images
In this paper, we propose Conditional Generative Model Identification (CGI), which aims to provide an effective way to identify the most suitable model using user-provided example images rather than requiring users to manually review a large number of models with example images.
BMIP: Bi-directional Modality Interaction Prompt Learning for VLM
Vision-language models (VLMs) have exhibited remarkable generalization capabilities, and prompt learning for VLMs has attracted great attention for the ability to adapt pre-trained VLMs to specific downstream tasks.
Robust Semi-Supervised Learning in Open Environments
Conventional SSL studies typically assume close environments where important factors (e. g., label, feature, distribution) between labeled and unlabeled data are consistent.
ChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning
Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of Language Agents.
You Only Submit One Image to Find the Most Suitable Generative Model
In this paper, we propose a novel setting called Generative Model Identification (GMI), which aims to enable the user to identify the most appropriate generative model(s) for the user's requirements from a large number of candidate models efficiently.
Fully Test-time Adaptation for Tabular Data
To this end, we propose the Fully Test-time Adaptation for Tabular data, namely FTAT, which enables FTTA methods to robustly optimize the label distribution of predictions, adapt to shifted covariate distributions, and suit a variety of tasks and models effectively.
Enabling Small Models for Zero-Shot Selection and Reuse through Model Label Learning
In this paper, we attempt to demonstrate that by constructing a model hub and aligning models with their functionalities using model labels, new tasks can be solved in a zero-shot manner by effectively selecting and reusing models in the hub.
Offline Imitation Learning with Model-based Reverse Augmentation
Specifically, we build a reverse dynamic model from the offline demonstrations, which can efficiently generate trajectories leading to the expert-observed states in a self-paced style.
LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks.
Robustness Assessment of Mathematical Reasoning in the Presence of Missing and Contradictory Conditions
To further study this problem, we develop a benchmark called Problems with Missing and Contradictory conditions (PMC) and introduce two novel metrics to evaluate the performance of few-shot prompting methods in these scenarios.
DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
In this paper, we explore a problem setting called Open-world Prompt Tuning (OPT), which involves tuning prompts on base classes and evaluating on a combination of base and new classes.
Investigating the Limitation of CLIP Models: The Worst-Performing Categories
Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
LAMDA-SSL: Semi-Supervised Learning in Python
The second part shows the usage of LAMDA-SSL by abundant examples in detail.
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
TRAS transforms the imbalanced pseudo-label distribution of a traditional SSL model via a delicate function to enhance the supervisory signals for minority classes.
Robust Deep Semi-Supervised Learning: A Brief Introduction
Semi-supervised learning (SSL) is the branch of machine learning that aims to improve learning performance by leveraging unlabeled data when labels are insufficient.
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data
However, in many real-world applications, it is desirable to have SSL algorithms that not only classify the samples drawn from the same distribution of labeled data but also detect out-of-distribution (OOD) samples drawn from an unknown distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement
For example, in user experience enhancement from Didi, one of the largest online ride-sharing platforms, the ride comment data contains severe label noise (due to the subjective factors of passengers) and severe label distribution bias (due to the sampling bias).
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
We guide the optimization of label quality through a small amount of validation data, and to ensure the safeness of performance while maximizing performance gain.
