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Found 29 papers, 11 papers with code
Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer
Gene mining is an important topic in the field of life sciences, but traditional machine learning methods cannot consider the regulatory relationships between genes.
Exploring the Mutual Influence between Self-Supervised Single-Frame and Multi-Frame Depth Estimation
Although both self-supervised single-frame and multi-frame depth estimation methods only require unlabeled monocular videos for training, the information they leverage varies because single-frame methods mainly rely on appearance-based features while multi-frame methods focus on geometric cues.
Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling
Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication.
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well.
3D Masked Autoencoding and Pseudo-labeling for Domain Adaptive Segmentation of Heterogeneous Infant Brain MRI
Robust segmentation of infant brain MRI across multiple ages, modalities, and sites remains challenging due to the intrinsic heterogeneity caused by different MRI scanners, vendors, or acquisition sequences, as well as varying stages of neurodevelopment.
Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point Clouds
Scene flow estimation, which predicts the 3D motion of scene points from point clouds, is a core task in autonomous driving and many other 3D vision applications.
Towards zero-shot Text-based voice editing using acoustic context conditioning, utterance embeddings, and reference encoders
Text-based voice editing (TBVE) uses synthetic output from text-to-speech (TTS) systems to replace words in an original recording.
Accurate and Efficient Stereo Matching via Attention Concatenation Volume
In this paper, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume.
Static Replication of Impermanent Loss for Concentrated Liquidity Provision in Decentralised Markets
This article analytically characterizes the impermanent loss of concentrated liquidity provision for automatic market makers in decentralised markets such as Uniswap.
Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving
Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue.
OneLabeler: A Flexible System for Building Data Labeling Tools
In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios.
Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks
While self-supervised speech representation learning has been popular in the speech research community, very few works have comprehensively analyzed audio representation learning for non-speech audio tasks.
Ranked #5 on
Audio Classification
on Balanced Audio Set
Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study
Detection of common events and scenes from audio is useful for extracting and understanding human contexts in daily life.
Do sound event representations generalize to other audio tasks? A case study in audio transfer learning
We also provide insights into the attributes of sound event representations that enable such efficient information transfer.
PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer
Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brain structures develop across stages of life.
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval
Then, a Wasserstein coupled dictionary, containing multiple pairs of counterpart graph keys with each key corresponding to one modality, is constructed for further feature learning.
An Infrared Communication System based on Handstand Pendulum
In this system, 940nm infrared light is mainly used for audio signal transmission, and an handstand pendulum based on PID is used to control the angle and stability of infrared light emission.
Instance-Aware Graph Convolutional Network for Multi-Label Classification
For label diffusion of instance-awareness in graph convolution, rather than using the statistical label correlation alone, an image-dependent label correlation matrix (LCM), fusing both the statistical LCM and an individual one of each image instance, is constructed for graph inference on labels to inject adaptive information of label-awareness into the learned features of the model.
Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization
Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary.
Learning Alignment for Multimodal Emotion Recognition from Speech
Further, emotion recognition will be beneficial from using audio-textual multimodal information, it is not trivial to build a system to learn from multimodality.
Multimodal Emotion Recognition
Speech Emotion Recognition
+2
TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain
Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection.
DELTA: A DEep learning based Language Technology plAtform
In this paper we present DELTA, a deep learning based language technology platform.
Ranked #3 on
Text Classification
on Yahoo! Answers
Connectionist Temporal Localization for Sound Event Detection with Sequential Labeling
Research on sound event detection (SED) with weak labeling has mostly focused on presence/absence labeling, which provides no temporal information at all about the event occurrences.
Sound Audio and Speech Processing
A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling
This paper compares five types of pooling functions both theoretically and experimentally, with special focus on their performance of localization.
Sound Audio and Speech Processing
ATLAS Probe: Breakthrough Science of Galaxy Evolution, Cosmology, Milky Way, and the Solar System
ATLAS Probe will lead to transformative science over the entire range of astrophysics: from galaxy evolution to the dark Universe, from Solar System objects to the dusty regions of the Milky Way.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics
The Symmetry of a Simple Optimization Problem in Lasso Screening
Recently dictionary screening has been proposed as an effective way to improve the computational efficiency of solving the lasso problem, which is one of the most commonly used method for learning sparse representations.
Feedback-Controlled Sequential Lasso Screening
In this context, we propose and explore a feedback controlled sequential screening scheme.
Screening Tests for Lasso Problems
For a given target vector, dictionary screening quickly identifies a subset of dictionary columns that will receive zero weight in a solution of the corresponding lasso problem.
Unsupervised Feature Learning by Deep Sparse Coding
In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks.
