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Found 22 papers, 11 papers with code
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
To this end, based on a given CNN model, we first generate a CNN architecture space in which each architecture is a multi-stage CNN generated from the given model using some predefined transformations.
Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker
Second, a novel cost matrix is formulated to adaptively fuse motion and appearance information, leveraging localization confidence and detection confidence as weighting factors.
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection
Model selection has been raised as an essential problem in the area of time series anomaly detection (TSAD), because there is no single best TSAD model for the highly heterogeneous time series in real-world applications.
Safe Distributed Learning-Enhanced Predictive Control for Multiple Quadrupedal Robots
Quadrupedal robots exhibit remarkable adaptability in unstructured environments, making them well-suited for formation control in real-world applications.
Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens
Recent advancements in large language models (LLMs) have driven significant progress in zero-shot text-to-speech (TTS) synthesis.
Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction
To mitigate the loss of intelligence during pre-training and preserve the original capabilities of the LLM, we propose a two-stage pre-training strategy that maintains language understanding while enhancing audio modeling.
Baichuan-Omni-1.5 Technical Report
We introduce Baichuan-Omni-1. 5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities.
Med-R$^2$: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based Medicine
Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios.
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs
Despite their potential, existing work mainly focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains.
Baichuan-Omni Technical Report
The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpart.
Ranked #20 on
Visual Question Answering
on MM-Vet
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
Through extensive experimental analysis, we identified three key challenges in designing effective data management strategies that enable the model to achieve long-context capability without sacrificing performance in other tasks: (1) a shortage of long documents across multiple domains, (2) effective construction of context windows, and (3) efficient organization of large-scale datasets.
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions.
Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning
In contrast to conventional methods that fuse features from multiple modalities, our proposed approach simplifies the neural architecture by retaining a single time series encoder, consequently leading to preserved scalability.
Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer
Despite advancements of end-to-end (E2E) models in speech recognition, named entity recognition (NER) is still challenging but critical for semantic understanding.
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation
Recently, end-to-end (E2E) automatic speech recognition (ASR) models have made great strides and exhibit excellent performance in general speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning
Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels.
UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation Learning
Machine learning has emerged as a powerful tool for time series analysis.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network
In our work, we present Auto-CASH, a pre-trained model based on meta-learning, to solve the CASH problem more efficiently.
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN).
Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images
Different from most of previous underwater image enhancement methods that compute light attenuation along object-camera path through hazy image formation model, we propose a novel jointly wavelength compensation and dehazing network (JWCDN) that takes into account the wavelength attenuation along surface-object path and the scattering along object-camera path simultaneously.
