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Found 42 papers, 18 papers with code
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
3D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capability
Our method utilizes implicit functions as the 3D shape representation and combines a novel latent-space GAN with a linear subspace model to discover semantic dimensions in the local latent space of 3D shapes.
Deep Active Learning with Structured Neural Depth Search
Previous work optimizes traditional active learning (AL) processes with incremental neural network architecture search (Active-iNAS) based on data complexity change, which improves the accuracy and learning efficiency.
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
Learning high-quality Q-value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) algorithms.
FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework
Flight Trajectory Prediction (FTP) is an essential task in Air Traffic Control (ATC), which can assist air traffic controllers to manage airspace more safely and efficiently.
SIA-FTP: A Spoken Instruction Aware Flight Trajectory Prediction Framework
A major reason is that spoken instructions and flight trajectories are presented in different modalities in the current air traffic control (ATC) system, bringing great challenges to considering the maneuvering instruction in the FTP tasks.
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning
In physical robotic experiments, our grasping framework grasped single known objects and novel complex-shaped household objects with a success rate of 90. 91%.
Towards Accurate Acne Detection via Decoupled Sequential Detection Head
In addition, we build a high-quality acne detection dataset named ACNE-DET to verify the effectiveness of DSDH.
Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection
In this paper, we propose a deep learning framework for generating acoustic feature embeddings sensitive to vocal quality and robust across different corpora.
Embedded Silicon-Organic Integrated Neuromorphic System
The development of artificial intelligence (AI) and robotics are both based on the tenet of "science and technology are people-oriented", and both need to achieve efficient communication with the human brain.
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture
To address the problem, an improved augmentation search strategy, named Augmented Density Matching, was proposed by randomly sampling policies from a prior distribution for training.
Learning High-quality Proposals for Acne Detection
Acne detection is crucial for interpretative diagnosis and precise treatment of skin disease.
Knowledge Distillation of Transformer-based Language Models Revisited
In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia.
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.
Speech recognition for air traffic control via feature learning and end-to-end training
In this work, we propose a new automatic speech recognition (ASR) system based on feature learning and an end-to-end training procedure for air traffic control (ATC) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches
Most importantly, a multi-modal speaker role identification network (MMSRINet) is designed to achieve the SRI task by considering both the speech and textual modality features.
Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry.
Restoring degraded speech via a modified diffusion model
We replace the mel-spectrum upsampler in DiffWave with a deep CNN upsampler, which is trained to alter the degraded speech mel-spectrum to match that of the original speech.
CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds
In contrast, this domain gap is considerably smaller and easier to fill for depth information.
M6: A Chinese Multimodal Pretrainer
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.
Dynamic Memory based Attention Network for Sequential Recommendation
It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.
Sparse-Interest Network for Sequential Recommendation
Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly.
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems
Finally, by integrating the SRL with ASR, an end-to-end multilingual ASR framework is formulated in a supervised manner, which is able to translate the raw wave into text in one model, i. e., wave-to-text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Q-SR: An Extensible Optimization Framework for Segment Routing
For the offline setting, we develop a fully polynomial time approximation scheme (FPTAS) which can finds a $(1+\omega)$-approximation solution for any specified $\omega>0$ in time that is a polynomial function of the network size.
Networking and Internet Architecture
A Survey on Machine Learning from Few Samples
The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability.
Cascade Convolutional Neural Network for Image Super-Resolution
With the development of the super-resolution convolutional neural network (SRCNN), deep learning technique has been widely applied in the field of image super-resolution.
Ranked #1 on
Image Super-Resolution
on BSD200 - 2x upscaling
Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation
In this paper, we collect all training data in a physics simulator and build an LSTM-based model to fit the pushing dynamics.
Robotics
Continuous Learning and Inference of Individual Probability of SARS-CoV-2 Infection Based on Interaction Data
This study presents a new approach to determine the likelihood of asymptomatic carriers of the SARS-CoV-2 virus by using interaction-based continuous learning and inference of individual probability (CLIIP) for contagious ranking.
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning has become prevalent in industrial recommender systems.
Controllable Multi-Interest Framework for Recommendation
Recent works usually give an overall embedding from a user's behavior sequence.
A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU
In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking method.
Robust Robotic Pouring using Audition and Haptics
Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment.
6D Object Pose Regression via Supervised Learning on Point Clouds
We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression.
Dimensional Reweighting Graph Convolution Networks
In this paper, we propose a method named Dimensional reweighting Graph Convolutional Networks (DrGCNs), to tackle the problem of variance between dimensional information in the node representations of GCNs.
Dimensional Reweighting Graph Convolutional Networks
Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs.
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many real-world applications.
Ranked #1 on
Link Prediction
on Alibaba
Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring
PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task.
Robotics Sound Audio and Speech Processing
PointNetGPD: Detecting Grasp Configurations from Point Sets
In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.
Robotics
Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network
In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands.
Robotics
Occlusion Resistant Object Rotation Regression from Point Cloud Segments
Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation.
Texture Object Segmentation Based on Affine Invariant Texture Detection
To solve the issue of segmenting rich texture images, a novel detection methods based on the affine invariable principle is proposed.
Saliency-guided Adaptive Seeding for Supervoxel Segmentation
We propose a new saliency-guided method for generating supervoxels in 3D space.
