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Found 9 papers, 0 papers with code
Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting
The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence reasoning and inference, the hurdle of incorporating multi-modal signals from historical news, financial knowledge graphs, etc., and the issue of interpreting and explaining the model results.
Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
An widely used approach to tackle this is to collect human annotation data and use them for evaluation or modeling.
Neural Stochastic Block Model & Scalable Community-Based Graph Learning
Compared with SBM, our framework is flexible, naturally allows soft labels and digestion of complex node attributes.
Pre-Training for Query Rewriting in A Spoken Language Understanding System
Then, inspired by the wide success of pre-trained contextual language embeddings, and also as a way to compensate for insufficient QR training data, we propose a language-modeling (LM) based approach to pre-train query embeddings on historical user conversation data with a voice assistant.
Knowledge Distillation from Internal Representations
In this paper, we propose to distill the internal representations of a large model such as BERT into a simplified version of it.
Correlated Anomaly Detection from Large Streaming Data
Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc.
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.
Ranked #16 on
Natural Language Inference
on SNLI
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario.
Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge
This paper presents a novel approach to the task of automatically inferring the most probable diagnosis from a given clinical narrative.
