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Found 19 papers, 10 papers with code
Baichuan4-Finance Technical Report
Large language models (LLMs) have demonstrated strong capabilities in language understanding, generation, and reasoning, yet their potential in finance remains underexplored due to the complexity and specialization of financial knowledge.
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
To address this question, we develop WaveToken, a wavelet-based tokenizer that allows models to learn complex representations directly in the space of time-localized frequencies.
UniMTS: Unified Pre-training for Motion Time Series
Motion time series collected from mobile and wearable devices such as smartphones and smartwatches offer significant insights into human behavioral patterns, with wide applications in healthcare, automation, IoT, and AR/XR due to their low-power, always-on nature.
Chronos: Learning the Language of Time Series
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
Large Language Models for Time Series: A Survey
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision.
Physics-Informed Data Denoising for Real-Life Sensing Systems
By obviating the need for ground truth clean data, our method offers a practical denoising solution for real-world applications.
PrimeNet: Pre-Training for Irregular Multivariate Time Series
In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time series.
Towards Diverse and Coherent Augmentation for Time-Series Forecasting
We note that augmentation designed for forecasting requires diversity as well as coherence with the original temporal dynamics.
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level.
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set.
Unleashing the Power of Shared Label Structures for Human Activity Recognition
In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities.
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years.
TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations
Task-oriented dialogue systems have been plagued by the difficulties of obtaining large-scale and high-quality annotated conversations.
Filling Conversation Ellipsis for Better Social Dialog Understanding
We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances.
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension
In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Neural Embeddings for Nearest Neighbor Search Under Edit Distance
The edit distance between two sequences is an important metric with many applications.
Proactive Human-Machine Conversation with Explicit Conversation Goal
Konv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.
Tucker Tensor Decomposition on FPGA
This paper presents an hardware accelerator for a classical tensor computation framework, Tucker decomposition.
Signal Processing Hardware Architecture
Proactive Human-Machine Conversation with Explicit Conversation Goals
DuConv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.
