✨SeeUPO: Sequence-Level Agentic-RL with Convergence Guarantees
📝 Summary:
SeeUPO is a critic-free reinforcement learning method that ensures convergence guarantees in multi-turn agent interactions by modeling sequential decision-making as multi-agent bandit problems and usi...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06554
• PDF: https://arxiv.org/pdf/2602.06554
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
SeeUPO is a critic-free reinforcement learning method that ensures convergence guarantees in multi-turn agent interactions by modeling sequential decision-making as multi-agent bandit problems and usi...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06554
• PDF: https://arxiv.org/pdf/2602.06554
==================================
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Forwarded from Machine Learning
🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
https://t.iss.one/DataScienceM
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
https://t.iss.one/DataScienceM
✨Avoiding Premature Collapse: Adaptive Annealing for Entropy-Regularized Structural Inference
📝 Summary:
Optimal transport models face premature mode collapse and instability during annealing as standard cooling is too fast. EPH-ASC, an adaptive algorithm, solves this by enforcing a linear stability law, preventing gradient explosions and stabilizing training.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23039
• PDF: https://arxiv.org/pdf/2601.23039
==================================
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📝 Summary:
Optimal transport models face premature mode collapse and instability during annealing as standard cooling is too fast. EPH-ASC, an adaptive algorithm, solves this by enforcing a linear stability law, preventing gradient explosions and stabilizing training.
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23039
• PDF: https://arxiv.org/pdf/2601.23039
==================================
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✨F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare
📝 Summary:
RLVR methods using group sampling suffer from bias toward likely trajectories and missed rare-correct ones; a difficulty-aware advantage scaling technique improves performance on benchmarks without in...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06717
• PDF: https://arxiv.org/pdf/2602.06717
==================================
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📝 Summary:
RLVR methods using group sampling suffer from bias toward likely trajectories and missed rare-correct ones; a difficulty-aware advantage scaling technique improves performance on benchmarks without in...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06717
• PDF: https://arxiv.org/pdf/2602.06717
==================================
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✨Seg-ReSearch: Segmentation with Interleaved Reasoning and External Search
📝 Summary:
Seg-ReSearch introduces a novel segmentation approach that combines interleaved reasoning with external search to overcome limitations of frozen MLLM knowledge, using hierarchical reward design for tr...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04454
• PDF: https://arxiv.org/pdf/2602.04454
• Github: https://github.com/iSEE-Laboratory/Seg-ReSearch
✨ Datasets citing this paper:
• https://huggingface.co/datasets/iSEE-Laboratory/OK_VOS
==================================
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📝 Summary:
Seg-ReSearch introduces a novel segmentation approach that combines interleaved reasoning with external search to overcome limitations of frozen MLLM knowledge, using hierarchical reward design for tr...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04454
• PDF: https://arxiv.org/pdf/2602.04454
• Github: https://github.com/iSEE-Laboratory/Seg-ReSearch
✨ Datasets citing this paper:
• https://huggingface.co/datasets/iSEE-Laboratory/OK_VOS
==================================
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✨Vision Transformer Finetuning Benefits from Non-Smooth Components
📝 Summary:
Vision transformer components exhibit varying plasticity levels that correlate with finetuning performance, challenging the assumption that smoothness is always beneficial. AI-generated summary The sm...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06883
• PDF: https://arxiv.org/pdf/2602.06883
• Github: https://github.com/ambroiseodt/vit-plasticity
==================================
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📝 Summary:
Vision transformer components exhibit varying plasticity levels that correlate with finetuning performance, challenging the assumption that smoothness is always beneficial. AI-generated summary The sm...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06883
• PDF: https://arxiv.org/pdf/2602.06883
• Github: https://github.com/ambroiseodt/vit-plasticity
==================================
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❤1
✨AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders
📝 Summary:
AudioSAE applies sparse autoencoders to Whisper and HuBERT models, extracting stable acoustic and semantic features. These features disentangle information, reduce false speech detections, and correlate with human EEG, demonstrating practical utility.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05027
• PDF: https://arxiv.org/pdf/2602.05027
==================================
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#AudioAI #SparseAutoencoders #MachineLearning #SpeechRecognition #Neuroscience
📝 Summary:
AudioSAE applies sparse autoencoders to Whisper and HuBERT models, extracting stable acoustic and semantic features. These features disentangle information, reduce false speech detections, and correlate with human EEG, demonstrating practical utility.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05027
• PDF: https://arxiv.org/pdf/2602.05027
==================================
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#AudioAI #SparseAutoencoders #MachineLearning #SpeechRecognition #Neuroscience
✨Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing
📝 Summary:
Group-Evolving Agents GEA enable open-ended self-improvement by treating agent groups as evolutionary units, allowing efficient experience sharing. GEA significantly outperforms state-of-the-art self-evolving methods on coding benchmarks, demonstrating enhanced robustness and sustained progress.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04837
• PDF: https://arxiv.org/pdf/2602.04837
==================================
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#AI #EvolutionaryAI #MultiAgentSystems #OpenEndedLearning #MachineLearning
📝 Summary:
Group-Evolving Agents GEA enable open-ended self-improvement by treating agent groups as evolutionary units, allowing efficient experience sharing. GEA significantly outperforms state-of-the-art self-evolving methods on coding benchmarks, demonstrating enhanced robustness and sustained progress.
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04837
• PDF: https://arxiv.org/pdf/2602.04837
==================================
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✨Urban Spatio-Temporal Foundation Models for Climate-Resilient Housing: Scaling Diffusion Transformers for Disaster Risk Prediction
📝 Summary:
This paper presents Skjold-DiT, a diffusion-transformer framework predicting building-level climate risks. It integrates urban data and transportation networks to generate accessibility layers for emergency response and intelligent vehicles. Experiments evaluate its prediction quality and cross-c...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06129
• PDF: https://arxiv.org/pdf/2602.06129
==================================
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#UrbanAI #ClimateResilience #DisasterRisk #DiffusionModels #SpatioTemporalAI
📝 Summary:
This paper presents Skjold-DiT, a diffusion-transformer framework predicting building-level climate risks. It integrates urban data and transportation networks to generate accessibility layers for emergency response and intelligent vehicles. Experiments evaluate its prediction quality and cross-c...
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06129
• PDF: https://arxiv.org/pdf/2602.06129
==================================
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✨Canzona: A Unified, Asynchronous, and Load-Balanced Framework for Distributed Matrix-based Optimizers
📝 Summary:
Canzona presents a unified asynchronous framework that addresses the conflict between matrix-based optimizers and distributed tensor fragmentation in LLM training, improving efficiency and reducing la...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06079
• PDF: https://arxiv.org/pdf/2602.06079
==================================
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📝 Summary:
Canzona presents a unified asynchronous framework that addresses the conflict between matrix-based optimizers and distributed tensor fragmentation in LLM training, improving efficiency and reducing la...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06079
• PDF: https://arxiv.org/pdf/2602.06079
==================================
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✨QuantLRM: Quantization of Large Reasoning Models via Fine-Tuning Signals
📝 Summary:
QuantLRM improves Large Reasoning Model quantization by using weight update magnitudes from fine-tuning to estimate channel importance. It protects both smallest and largest updates, consistently outperforming traditional methods and applying even to non-fine-tuned models.
🔹 Publication Date: Published on Jan 31
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02581
• PDF: https://arxiv.org/pdf/2602.02581
• Github: https://github.com/psunlpgroup/QuantLRM
🔹 Models citing this paper:
• https://huggingface.co/nanzhang/QuantLRM-R1-Qwen-32B-3-bit
• https://huggingface.co/nanzhang/QuantLRM-R1-Llama-70B-3-bit
• https://huggingface.co/nanzhang/QuantLRM-R1-Qwen3-8B-3-bit
==================================
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#Quantization #LargeLanguageModels #DeepLearning #AI #ModelCompression
📝 Summary:
QuantLRM improves Large Reasoning Model quantization by using weight update magnitudes from fine-tuning to estimate channel importance. It protects both smallest and largest updates, consistently outperforming traditional methods and applying even to non-fine-tuned models.
🔹 Publication Date: Published on Jan 31
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02581
• PDF: https://arxiv.org/pdf/2602.02581
• Github: https://github.com/psunlpgroup/QuantLRM
🔹 Models citing this paper:
• https://huggingface.co/nanzhang/QuantLRM-R1-Qwen-32B-3-bit
• https://huggingface.co/nanzhang/QuantLRM-R1-Llama-70B-3-bit
• https://huggingface.co/nanzhang/QuantLRM-R1-Qwen3-8B-3-bit
==================================
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#Quantization #LargeLanguageModels #DeepLearning #AI #ModelCompression
✨Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion
📝 Summary:
Table-as-Search TaS reformulates information seeking as table completion to robustly manage long-horizon search states. By mapping queries to structured tables, TaS explicitly tracks progress and plans, significantly outperforming baselines in complex search tasks.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06724
• PDF: https://arxiv.org/pdf/2602.06724
• Github: https://github.com/AIDC-AI/Marco-DeepResearch/
==================================
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#AI #InformationRetrieval #AgenticAI #TableCompletion #SearchAlgorithms
📝 Summary:
Table-as-Search TaS reformulates information seeking as table completion to robustly manage long-horizon search states. By mapping queries to structured tables, TaS explicitly tracks progress and plans, significantly outperforming baselines in complex search tasks.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06724
• PDF: https://arxiv.org/pdf/2602.06724
• Github: https://github.com/AIDC-AI/Marco-DeepResearch/
==================================
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#AI #InformationRetrieval #AgenticAI #TableCompletion #SearchAlgorithms
❤1
✨OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention
📝 Summary:
OmniVideo-R1 is a reinforced framework that enhances audio-visual understanding. It uses self-supervised query-intensive grounding and contrastive modality-attentive fusion. Experiments show OmniVideo-R1 consistently outperforms baselines, demonstrating its effectiveness.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05847
• PDF: https://arxiv.org/pdf/2602.05847
==================================
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#AudioVisualAI #SelfSupervisedLearning #DeepLearning #MultimodalAI #AIResearch
📝 Summary:
OmniVideo-R1 is a reinforced framework that enhances audio-visual understanding. It uses self-supervised query-intensive grounding and contrastive modality-attentive fusion. Experiments show OmniVideo-R1 consistently outperforms baselines, demonstrating its effectiveness.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.05847
• PDF: https://arxiv.org/pdf/2602.05847
==================================
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✨SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
📝 Summary:
SEAD enables service dialogue agents to learn effective strategies through self-evolving, decoupled user modeling. This trains agents without large human annotations, significantly improving task completion and dialogue efficiency compared to existing models.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03548
• PDF: https://arxiv.org/pdf/2602.03548
• Github: https://github.com/Da1yuqin/SEAD
🔹 Models citing this paper:
• https://huggingface.co/dayll/SEAD-14B
==================================
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#AI #ConversationalAI #ReinforcementLearning #NLP #AIagents
📝 Summary:
SEAD enables service dialogue agents to learn effective strategies through self-evolving, decoupled user modeling. This trains agents without large human annotations, significantly improving task completion and dialogue efficiency compared to existing models.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03548
• PDF: https://arxiv.org/pdf/2602.03548
• Github: https://github.com/Da1yuqin/SEAD
🔹 Models citing this paper:
• https://huggingface.co/dayll/SEAD-14B
==================================
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#AI #ConversationalAI #ReinforcementLearning #NLP #AIagents
✨ReMiT: RL-Guided Mid-Training for Iterative LLM Evolution
📝 Summary:
ReMiT introduces a bidirectional training approach for LLMs. It leverages RL-guided mid-training to dynamically reweight tokens, improving pre-training performance and sustaining gains throughout post-training. This creates a self-reinforcing, iterative evolution cycle for LLMs.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03075
• PDF: https://arxiv.org/pdf/2602.03075
==================================
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#LLM #ReinforcementLearning #MachineLearning #AITraining #DeepLearning
📝 Summary:
ReMiT introduces a bidirectional training approach for LLMs. It leverages RL-guided mid-training to dynamically reweight tokens, improving pre-training performance and sustaining gains throughout post-training. This creates a self-reinforcing, iterative evolution cycle for LLMs.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03075
• PDF: https://arxiv.org/pdf/2602.03075
==================================
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#LLM #ReinforcementLearning #MachineLearning #AITraining #DeepLearning
✨Self-Improving World Modelling with Latent Actions
📝 Summary:
SWIRL learns world models from state-only data by treating actions as latent variables. It alternates forward and inverse dynamics modeling, using information maximization and ELBO, to achieve improved performance across diverse reasoning and planning tasks.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06130
• PDF: https://arxiv.org/pdf/2602.06130
==================================
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#WorldModels #ReinforcementLearning #LatentVariables #MachineLearning #AI
📝 Summary:
SWIRL learns world models from state-only data by treating actions as latent variables. It alternates forward and inverse dynamics modeling, using information maximization and ELBO, to achieve improved performance across diverse reasoning and planning tasks.
🔹 Publication Date: Published on Feb 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06130
• PDF: https://arxiv.org/pdf/2602.06130
==================================
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#WorldModels #ReinforcementLearning #LatentVariables #MachineLearning #AI
✨Pisets: A Robust Speech Recognition System for Lectures and Interviews
📝 Summary:
Pisets is a robust Russian speech-to-text system combining Wav2Vec2, AST, and Whisper models. It uses curriculum learning and uncertainty modeling to improve accuracy and reduce hallucinations for long audio, outperforming other Whisper variants.
🔹 Publication Date: Published on Jan 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18415
• PDF: https://arxiv.org/pdf/2601.18415
🔹 Models citing this paper:
• https://huggingface.co/bond005/wav2vec2-large-ru-golos
• https://huggingface.co/bond005/whisper-large-v3-ru-podlodka
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ehristoforu/server0001
• https://huggingface.co/spaces/dimafatality/bond005-wav2vec2-large-ru-golos
• https://huggingface.co/spaces/PatrickRedStar/video_image
==================================
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#SpeechRecognition #AI #MachineLearning #NLP #WhisperAI
📝 Summary:
Pisets is a robust Russian speech-to-text system combining Wav2Vec2, AST, and Whisper models. It uses curriculum learning and uncertainty modeling to improve accuracy and reduce hallucinations for long audio, outperforming other Whisper variants.
🔹 Publication Date: Published on Jan 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18415
• PDF: https://arxiv.org/pdf/2601.18415
🔹 Models citing this paper:
• https://huggingface.co/bond005/wav2vec2-large-ru-golos
• https://huggingface.co/bond005/whisper-large-v3-ru-podlodka
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ehristoforu/server0001
• https://huggingface.co/spaces/dimafatality/bond005-wav2vec2-large-ru-golos
• https://huggingface.co/spaces/PatrickRedStar/video_image
==================================
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#SpeechRecognition #AI #MachineLearning #NLP #WhisperAI
❤1
✨compar:IA: The French Government's LLM arena to collect French-language human prompts and preference data
📝 Summary:
Compar:IA is a French government open-source platform collecting large-scale French human preference data for LLM training. It addresses the scarcity of non-English data via a blind pairwise comparison interface and releases three datasets, aiming to be an international public good.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06669
• PDF: https://arxiv.org/pdf/2602.06669
• Project Page: https://comparia.beta.gouv.fr/
• Github: https://github.com/betagouv/ComparIA
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Compar:IA is a French government open-source platform collecting large-scale French human preference data for LLM training. It addresses the scarcity of non-English data via a blind pairwise comparison interface and releases three datasets, aiming to be an international public good.
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06669
• PDF: https://arxiv.org/pdf/2602.06669
• Project Page: https://comparia.beta.gouv.fr/
• Github: https://github.com/betagouv/ComparIA
==================================
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✨AtlasPatch: An Efficient and Scalable Tool for Whole Slide Image Preprocessing in Computational Pathology
📝 Summary:
AtlasPatch is an efficient and scalable tool for whole-slide image preprocessing. It uses a fine-tuned Segment-Anything model for accurate tissue detection and high-throughput patch extraction, significantly reducing computational overhead and matching state-of-the-art performance.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03998
• PDF: https://arxiv.org/pdf/2602.03998
🔹 Models citing this paper:
• https://huggingface.co/AtlasAnalyticsLab/AtlasPatch
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
AtlasPatch is an efficient and scalable tool for whole-slide image preprocessing. It uses a fine-tuned Segment-Anything model for accurate tissue detection and high-throughput patch extraction, significantly reducing computational overhead and matching state-of-the-art performance.
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03998
• PDF: https://arxiv.org/pdf/2602.03998
🔹 Models citing this paper:
• https://huggingface.co/AtlasAnalyticsLab/AtlasPatch
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤2
✨Learning a Generative Meta-Model of LLM Activations
📝 Summary:
Training diffusion models on neural network activations creates meta-models that learn internal state distributions and improve intervention fidelity without restrictive structural assumptions. AI-gen...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06964
• PDF: https://arxiv.org/pdf/2602.06964
• Github: https://github.com/g-luo/generative_latent_prior
🔹 Models citing this paper:
• https://huggingface.co/generative-latent-prior/glp-llama8b-d6
• https://huggingface.co/generative-latent-prior/glp-llama1b-d3
• https://huggingface.co/generative-latent-prior/glp-llama1b-d6
✨ Datasets citing this paper:
• https://huggingface.co/datasets/generative-latent-prior/frechet-distance-fineweb-50k
• https://huggingface.co/datasets/generative-latent-prior/llama8b-layer15-sae-probes
• https://huggingface.co/datasets/generative-latent-prior/llama1b-layer07-fineweb-1M
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Training diffusion models on neural network activations creates meta-models that learn internal state distributions and improve intervention fidelity without restrictive structural assumptions. AI-gen...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06964
• PDF: https://arxiv.org/pdf/2602.06964
• Github: https://github.com/g-luo/generative_latent_prior
🔹 Models citing this paper:
• https://huggingface.co/generative-latent-prior/glp-llama8b-d6
• https://huggingface.co/generative-latent-prior/glp-llama1b-d3
• https://huggingface.co/generative-latent-prior/glp-llama1b-d6
✨ Datasets citing this paper:
• https://huggingface.co/datasets/generative-latent-prior/frechet-distance-fineweb-50k
• https://huggingface.co/datasets/generative-latent-prior/llama8b-layer15-sae-probes
• https://huggingface.co/datasets/generative-latent-prior/llama1b-layer07-fineweb-1M
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
Learning a Generative Meta-Model of LLM Activations
Existing approaches for analyzing neural network activations, such as PCA and sparse autoencoders, rely on strong structural assumptions. Generative models offer an alternative: they can uncover...