Search Results for author:
Found 28 papers, 13 papers with code
Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones
Remote photoplethysmography (rPPG) emerges as a promising method for non-invasive, convenient measurement of vital signs, utilizing the widespread presence of cameras.
Ground-to-UAV 140 GHz channel measurement and modeling
Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance.
An Interpretable Power System Transient Stability Assessment Method with Expert Guiding Neural-Regression-Tree
TSA-ENRT utilizes an expert guiding nonlinear regression tree to approximate the neural network prediction and the neural network can be explained by the interpretive rules generated by the tree model.
FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detecction
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content.
Explain Variance of Prediction in Variational Time Series Models for Clinical Deterioration Prediction
To achieve this goal, we propose variance SHAP with variational time series models, an application of Shapley Additive Expanation(SHAP) algorithm to attribute epistemic prediction uncertainty.
A Kalman Filter Based Framework for Monitoring the Performance of In-Hospital Mortality Prediction Models Over Time
Therefore, in this study, for binary classifiers running in a long time period, we proposed to adjust these performance metrics for sample size and class distribution, so that a fair comparison can be made between two time periods.
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
Finally, we use this model to filter out inconsistent model-generated rationales in self-consistency decoding, resulting in a 6% accuracy improvement on average across three MCQ datasets.
Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens
Second, existing $n$-gram LMs use small $n$ which hinders their performance; we instead allow $n$ to be arbitrarily large, by introducing a new $\infty$-gram LM with backoff.
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
Crystal: Introspective Reasoners Reinforced with Self-Feedback
Extensive work has shown that the performance and interpretability of commonsense reasoning can be improved via knowledge-augmented reasoning methods, where the knowledge that underpins the reasoning process is explicitly verbalized and utilized.
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
To bridge this gap, we present MathVista, a benchmark designed to combine challenges from diverse mathematical and visual tasks.
Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding
The key idea is not to throw out the value network, a byproduct of PPO training for evaluating partial output sequences, when decoding text out of the policy network.
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints
Unlike cloud-based deep learning models that are often large and uniform, edge-deployed models usually demand customization for domain-specific tasks and resource-limited environments.
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method
The architecture of GTAGC encompasses graph embedding, integration of the Graph Transformer within the autoencoder structure, and a clustering component.
Inverse Scaling: When Bigger Isn't Better
Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e. g., due to flaws in the training objective and data.
Optimizing Investment Strategies with Lazy Factor and Probability Weighting: A Price Portfolio Forecasting and Mean-Variance Model with Transaction Costs Approach
Our approach bifurcates into a Price Portfolio Forecasting Model and a Mean-Variance Model with Transaction Costs, utilizing probability weights as the coefficients of laziness factors.
Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements
Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures.
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems.
Ranked #3 on
Automated Theorem Proving
on miniF2F-valid
(Pass@100 metric)
Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering
Our work is the first to report that knowledge generated by models that are orders of magnitude smaller than GPT-3, even without direct supervision on the knowledge itself, can exceed the quality of commonsense knowledge elicited from GPT-3.
RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation
In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy.
NaturalProver: Grounded Mathematical Proof Generation with Language Models
Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence.
DataCLUE: A Benchmark Suite for Data-centric NLP
However, we have not seen significant research progress in this field, especially in NLP.
Generated Knowledge Prompting for Commonsense Reasoning
It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models.
NaturalProofs: Mathematical Theorem Proving in Natural Language
Understanding and creating mathematics using natural mathematical language - the mixture of symbolic and natural language used by humans - is a challenging and important problem for driving progress in machine learning.
Continual Deterioration Prediction for Hospitalized COVID-19 Patients
In this paper, we first verify the assumption that clinical variables could have time-varying effects on COVID-19 outcomes.
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
Therefore, we manually correct these label mistakes and form a cleaner test set.
Ranked #3 on
Named Entity Recognition (NER)
on CoNLL++
(using extra training data)
Phrase Grounding by Soft-Label Chain Conditional Random Field
In this paper, we formulate phrase grounding as a sequence labeling task where we treat candidate regions as potential labels, and use neural chain Conditional Random Fields (CRFs) to model dependencies among regions for adjacent mentions.
Ranked #8 on
Phrase Grounding
on Flickr30k Entities Test
