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Found 14 papers, 2 papers with code
AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
Introducing AgiBot World, a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios, we achieve an order-of-magnitude increase in data scale compared to existing datasets.
Phys4DGen: A Physics-Driven Framework for Controllable and Efficient 4D Content Generation from a Single Image
The task of 4D content generation involves creating dynamic 3D models that evolve over time in response to specific input conditions, such as images.
BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction
Retrosynthesis analysis is pivotal yet challenging in drug discovery and organic chemistry.
FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification
Inspired by the multi-granular diagnostic approach of pathologists, we perform feature extraction on cell structures at coarse, medium, and fine granularity, enabling the model to fully harness the information in histopathological images.
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model
Therefore, we propose a joint ABSA model, which not only enjoys the benefits of encoder sharing but also focuses on the difference to improve the effectiveness of the model.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization
Detailed ablation and visualization analysis are included to further demonstrate each of our proposed modules' effectiveness in our method.
Autonomous Driving
Data Augmentation
+1
Robotics
A Learning-Based Tune-Free Control Framework for Large Scale Autonomous Driving System Deployment
This paper presents the design of a tune-free (human-out-of-the-loop parameter tuning) control framework, aiming at accelerating large scale autonomous driving system deployed on various vehicles and driving environments.
Document-level Neural Machine Translation with Document Embeddings
Standard neural machine translation (NMT) is on the assumption of document-level context independent.
Document-level Neural Machine Translation with Associated Memory Network
Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network.
Controllable Dual Skew Divergence Loss for Neural Machine Translation
Our experiments indicate that switching to the DSD loss after the convergence of ML training helps models escape local optima and stimulates stable performance improvements.
Judging Chemical Reaction Practicality From Positive Sample only Learning
Chemical reaction practicality is the core task among all symbol intelligence based chemical information processing, for example, it provides indispensable clue for further automatic synthesis route inference.
Carrier Sentence Selection for Fill-in-the-blank Items
Fill-in-the-blank items are a common form of exercise in computer-assisted language learning systems.
Distractor Generation for Chinese Fill-in-the-blank Items
This paper reports the first study on automatic generation of distractors for fill-in-the-blank items for learning Chinese vocabulary.
A Reading Environment for Learners of Chinese as a Foreign Language
We present a mobile app that provides a reading environment for learners of Chinese as a foreign language.
