4DLidarOpen is a new open dataset providing synchronized 4D FMCW Lidar velocity measurements, multi-Lidar and camera data, and 3D bounding-box annotations with track IDs to support benchmarks on 3D detection, BEV segmentation, flow prediction, and motion forecasting.
Multi-task learning with deep neural networks: A survey
7 Pith papers cite this work. Polarity classification is still indexing.
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CODI compresses explicit CoT into continuous space via self-distillation and is the first implicit method to match explicit CoT performance on GSM8k at GPT-2 scale with 3.1x compression and 28.2% higher accuracy than prior implicit approaches.
USTri is a tri-stage ultrasound system that trains a generalist model, fine-tunes specialists while frozen, and deploys an agent for workflow orchestration, claiming top performance across 4 task types and 27 datasets.
FedRouter clusters adapters locally per task samples and globally across clients to create task-centric personalized models, improving generalization and reducing task interference in federated fine-tuning.
Introduces progressive task-specific multi-task adaptation for vision transformers, sharing adapters early and specializing later with gradient-based task allocation, outperforming prior methods on PASCAL and NYUD-v2 with fewer trainable parameters.
Derives tighter generalization bounds for vector-valued neural networks and deep kernel methods in multi-task learning via Koopman and PF operators, with sketching for efficiency and a new vvRKHS framework.
MacroNav learns multi-scale navigation-centric representations through multi-task self-supervised learning and combines them with graph-based reinforcement learning for efficient action selection, reporting gains in success rate and path efficiency over prior methods.
citing papers explorer
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4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving
4DLidarOpen is a new open dataset providing synchronized 4D FMCW Lidar velocity measurements, multi-Lidar and camera data, and 3D bounding-box annotations with track IDs to support benchmarks on 3D detection, BEV segmentation, flow prediction, and motion forecasting.
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CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation
CODI compresses explicit CoT into continuous space via self-distillation and is the first implicit method to match explicit CoT performance on GSM8k at GPT-2 scale with 3.1x compression and 28.2% higher accuracy than prior implicit approaches.
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Unified Ultrasound Intelligence Toward an End-to-End Agentic System
USTri is a tri-stage ultrasound system that trains a generalist model, fine-tunes specialists while frozen, and deploys an agent for workflow orchestration, claiming top performance across 4 task types and 27 datasets.
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Task-Centric Personalized Federated Fine-Tuning of Language Models
FedRouter clusters adapters locally per task samples and globally across clients to create task-centric personalized models, improving generalization and reducing task interference in federated fine-tuning.
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Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation
Introduces progressive task-specific multi-task adaptation for vision transformers, sharing adapters early and specializing later with gradient-based task allocation, outperforming prior methods on PASCAL and NYUD-v2 with fewer trainable parameters.
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Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning
Derives tighter generalization bounds for vector-valued neural networks and deep kernel methods in multi-task learning via Koopman and PF operators, with sketching for efficiency and a new vvRKHS framework.
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MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments
MacroNav learns multi-scale navigation-centric representations through multi-task self-supervised learning and combines them with graph-based reinforcement learning for efficient action selection, reporting gains in success rate and path efficiency over prior methods.