Search Results for author:
Found 12 papers, 0 papers with code
Synthetic Textual Features for the Large-Scale Detection of Basic-level Categories in English and Mandarin
The best of our methods outperforms the current SoA in BLC detection, with an accuracy of English BLC detection of 75. 0%, and of Mandarin BLC detection 80. 7% on a test set.
StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields
In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer.
Deep-learning-aided Low-complexity DOA Estimators for Ultra-Massive MIMO Overlapped Receive Array
Massive multiple input multiple output(MIMO)-based fully-digital receive antenna arrays bring huge amount of complexity to both traditional direction of arrival(DOA) estimation algorithms and neural network training, which is difficult to satisfy high-precision and low-latency applications in future wireless communications.
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
Results show that our method can generate shapes consistent with text descriptions, and shapes evolve gradually as more phrases are added.
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation
Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping.
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning
Previous methods achieved good performance on such manipulation tasks.
Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array
In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP.
Two Low-complexity DOA Estimators for Massive/Ultra-massive MIMO Receive Array
Eigen-decomposition-based direction finding methods of using large-scale/ultra-large-scale fully-digital receive antenna arrays lead to a high or ultra-high complexity.
A Versatile Agent for Fast Learning from Human Instructors
In recent years, a myriad of superlative works on intelligent robotics policies have been done, thanks to advances in machine learning.
Improving Learning from Demonstrations by Learning from Experience
How to make imitation learning more general when demonstrations are relatively limited has been a persistent problem in reinforcement learning (RL).
Canoe : A System for Collaborative Learning for Neural Nets
For highly distributed environments such as edge computing, collaborative learning approaches eschew the dependence on a global, shared model, in favor of models tailored for each location.
GenQu: A Hybrid Framework for Learning Classical Data in Quantum States
In this paper, we propose GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states.
