pwc-archive/papers-with-abstracts huggingface dataset:
SCU-NLP at ROCLING 2022 Shared Task: Experiment and Error Analysis of Biomedical Entity Detection Model
Authors: Sung-Ting Chiou, Sheng-Wei Huang, Ying-Chun Lo, Yu-Hsuan Wu, Jheng-Long Wu
Date: 2022-11-01 00:00:00
Proceeding: ROCLING 2022 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition generally refers to entities with specific meanings in unstructured text, including names of people, places, organizations, dates, times, quantities, proper nouns and other words. In the medical field, it may be drug names, Organ names, test items, nutritional supplements, etc. The purpose of named entity recognition in this study is to search for the above items from unstructured input text. In this study, taking healthcare as the research purpose, and predicting named entity boundaries and categories of sentences based on ten entity types, We explore multiple fundamental NER approaches to solve this task, Include: Hidden Markov Models, Conditional Random Fields, Random Forest Classifier and BERT. The prediction results are more significant in the F-score of the CRF model, and have achieved better results.
MIGBaseline at ROCLING 2022 Shared Task: Report on Named Entity Recognition Using Chinese Healthcare Datasets
Authors: Hsing-Yuan Ma, Wei-Jie Li, Chao-Lin Liu
Date: 2022-11-01 00:00:00
Proceeding: ROCLING 2022 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER) tools have been in development for years, yet few have been aimed at medical documents. The increasing needs for analyzing medical data makes it crucial to build a sophisticated NER model for this missing area. In this paper, W2NER, the state-of-the-art NER model, which has excelled in English and Chinese tasks, is run through selected inputs, several pretrained language models, and training strategies. The objective was to build an NER model suitable for healthcare corpora in Chinese. The best model managed to achieve an F1 score at 81.93%, which ranked first in the ROCLING 2022 shared task.
Overview of the ROCLING 2022 Shared Task for Chinese Healthcare Named Entity Recognition
Authors: Lung-Hao Lee, Chao-Yi Chen, Liang-Chih Yu, Yuen-Hsien Tseng
Date: 2022-11-01 00:00:00
Proceeding: ROCLING 2022 11
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper describes the ROCLING-2022 shared task for Chinese healthcare named entity recognition, including task description, data preparation, performance metrics, and evaluation results. Among ten registered teams, seven participating teams submitted a total of 20 runs. This shared task reveals present NLP techniques for dealing with Chinese named entity recognition in the healthcare domain. All data sets with gold standards and evaluation scripts used in this shared task are publicly available for future research.
AGNAS: Attention-Guided Micro- and Macro-Architecture Search
Authors: Zihao Sun, Yu Hu, Shun Lu, Longxing Yang, Jilin Mei, Yinhe Han, Xiaowei Li
Date: 2022-06-05 00:00:00
Proceeding: International Conference on Machine Learning 2022 6
Tasks: Neural Architecture Search

Micro- and macro-architecture search have emerged as two popular NAS paradigms recently. Existing methods leverage different search strategies for searching micro- and macro- architectures. When using architecture parameters to search for micro-structure such as normal cell and reduction cell, the architecture parameters can not fully reflect the corresponding operation importance. When searching for the macro-structure chained by pre-defined blocks, many sub-networks need to be sampled for evaluation, which is very time-consuming. To address the two issues, we propose a new search paradigm, that is, leverage the attention mechanism to guide the micro- and macro-architecture search, namely AGNAS. Specifically, we introduce an attention module and plug it behind each candidate operation or each candidate block. We utilize the attention weights to represent the importance of the relevant operations for the micro search or the importance of the relevant blocks for the macro search. Experimental results show that AGNAS can achieve 2.46% test error on CIFAR-10 in the DARTS search space, and 23.4% test error when directly searching on ImageNet in the ProxylessNAS search space. AGNAS also achieves optimal performance on NAS-Bench-201, outperforming state-of-the-art approaches
M3TR: Multi-modal Multi-label Recognition with Transformer
Authors: Jiawei Zhao, Yifan Zhao, Jia Li
Date: 2021-10-01 00:00:00
Proceeding: ACM MM 2021 10
Tasks: Multi-Label Classification, Multi-Label Image Recognition

Multi-label image recognition aims to recognize multiple objects simultaneously in one image. Recent ideas to solve this problem have focused on learning dependencies of label co-occurrences to enhance the high-level semantic representations. However, these methods usually neglect the important relations of intrinsic visual structures and face difficulties in understanding contextual relationships. To build the global scope of visual context as well as interactions between visual modality and linguistic modality, we propose the Multi-Modal Multi-label recognition TRansformers (M3TR) with the ternary relationship learning for inter-and intra-modalities. For the intra-modal relationship, we make insightful conjunction of CNNs and Transformers, which embeds visual structures into the high-level features by learning the semantic cross-attention. For constructing the interactions between the visual and linguistic modalities, we propose a linguistic cross-attention to embed the class-wise linguistic information into the visual structure learning, and finally present a linguistic guided enhancement module to enhance the representation of high-level semantics. Experimental evidence reveals that with the collaborative learning of ternary relationship, our proposed M3TR achieves new state-of-the-art on two public multi-label recognition benchmarks.
Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping
Authors: Richard S. Sutton, Csaba Szepesvari, Alborz Geramifard, Michael P. Bowling
Date: 2012-06-13 00:00:00

We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available after each interaction with the world. This paper develops an explicitly model-based approach extending the Dyna architecture to linear function approximation. Dynastyle planning proceeds by generating imaginary experience from the world model and then applying model-free reinforcement learning algorithms to the imagined state transitions. Our main results are to prove that linear Dyna-style planning converges to a unique solution independent of the generating distribution, under natural conditions. In the policy evaluation setting, we prove that the limit point is the least-squares (LSTD) solution. An implication of our results is that prioritized-sweeping can be soundly extended to the linear approximation case, backing up to preceding features rather than to preceding states. We introduce two versions of prioritized sweeping with linear Dyna and briefly illustrate their performance empirically on the Mountain Car and Boyan Chain problems.
Data-driven modelling of energy demand response behaviour based on a large-scale residential trial
Authors: Ioannis Antonopoulos, Valentin Robuab, Benoit Couraud, David Flynn
Date: 2021-06-01 00:00:00
Proceeding: Energy and AI 2021 6

Recent years have seen an increasing interest in Demand Response (DR), as a means to satisfy the growing flexibility needs of modern power grids. This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix, and increasing complexity in demand profiles from the electrification of transport networks. Currently, less than 2% of the global potential for demand-side flexibility is currently utilised, but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential. In order to achieve this target, acquiring a better understanding of how residential DR participants respond in DR events is essential – and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge. This study provides an in-depth analysis of how residential customers have responded in incentive-based DR, utilising household-related data from a large-scale, real-world trial: the Smart Grid, Smart City (SGSC) project. Using a number of different machine learning approaches, we model the relationship between a household’s response and household-related features. Moreover, we examine the potential effects of households’ features on the residential response behaviour, and highlight a number of key insights which raise questions about the reported level of consumers’ engagement in DR schemes, and the motivation for different customers’ response level. Finally, we explore the temporal structure of the response – and although we found no supporting evidence of DR responders learning over time for the available data from this trial, the proposed methodologies could be used for longer-term longitudinal DR studies. Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area.
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Authors: Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, Song Han
Date: 2022-11-18 00:00:00
Tasks: Quantization

Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods cannot maintain accuracy and hardware efficiency at the same time. We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs. Based on the fact that weights are easy to quantize while activations are not, SmoothQuant smooths the activation outliers by offline migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation. SmoothQuant enables an INT8 quantization of both weights and activations for all the matrix multiplications in LLMs, including OPT, BLOOM, GLM, MT-NLG, Llama-1/2, Falcon, Mistral, and Mixtral models. We demonstrate up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy. SmoothQuant enables serving 530B LLM within a single node. Our work offers a turn-key solution that reduces hardware costs and democratizes LLMs. Code is available at https://github.com/mit-han-lab/smoothquant.
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation
Authors: Ben Dai, Xiaotong Shen, Lin Yee Chen, Chunlin Li, Wei Pan
Date: 2022-11-18 00:00:00
Tasks: Decision Making, Explainable artificial intelligence, Visual Localization

In explainable artificial intelligence, discriminative feature localization is critical to reveal a blackbox model's decision-making process from raw data to prediction. In this article, we use two real datasets, the MNIST handwritten digits and MIT-BIH Electrocardiogram (ECG) signals, to motivate key characteristics of discriminative features, namely adaptiveness, predictive importance and effectiveness. Then, we develop a localization framework based on adversarial attacks to effectively localize discriminative features. In contrast to existing heuristic methods, we also provide a statistically guaranteed interpretability of the localized features by measuring a generalized partial $R^2$. We apply the proposed method to the MNIST dataset and the MIT-BIH dataset with a convolutional auto-encoder. In the first, the compact image regions localized by the proposed method are visually appealing. Similarly, in the second, the identified ECG features are biologically plausible and consistent with cardiac electrophysiological principles while locating subtle anomalies in a QRS complex that may not be discernible by the naked eye. Overall, the proposed method compares favorably with state-of-the-art competitors. Accompanying this paper is a Python library dnn-locate (https://dnn-locate.readthedocs.io/en/latest/) that implements the proposed approach.
Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach
Authors: Siddhant Bhambri, Amrita Bhattacharjee, Dimitri Bertsekas
Date: 2022-11-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we address the solution of the popular Wordle puzzle, using new reinforcement learning methods, which apply more generally to adaptive control of dynamic systems and to classes of Partially Observable Markov Decision Process (POMDP) problems. These methods are based on approximation in value space and the rollout approach, admit a straightforward implementation, and provide improved performance over various heuristic approaches. For the Wordle puzzle, they yield on-line solution strategies that are very close to optimal at relatively modest computational cost. Our methods are viable for more complex versions of Wordle and related search problems, for which an optimal strategy would be impossible to compute. They are also applicable to a wide range of adaptive sequential decision problems that involve an unknown or frequently changing environment whose parameters are estimated on-line.
Deep Gaussian Processes for Air Quality Inference
Authors: Aadesh Desai, Eshan Gujarathi, Saagar Parikh, Sachin Yadav, Zeel Patel, Nipun Batra
Date: 2022-11-18 00:00:00
Tasks: Air Quality Inference, Gaussian Processes, Variational Inference

Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored locations is crucial. Conventional interpolation methods fail to learn the complex AQ phenomena. This work demonstrates that Deep Gaussian Process models (DGPs) are a promising model for the task of AQ inference. We implement Doubly Stochastic Variational Inference, a DGP algorithm, and show that it performs comparably to the state-of-the-art models.
Universal Successor Features Based Deep Reinforcement Learning for Navigation
Authors: Shamane Siriwardhana
Date: 2019-06-17 00:00:00
Proceeding: Master's Thesis - UOA 2019 6
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation, Transfer Learning, Visual Navigation

This thesis outlines a research work on modeling a novel approach for robot navigation by using an advanced Deep Reinforcement Learning (DRL) algorithm. Visual navigation is a core problem in robotics and machine vision. Previous research used map-based, map-building, or map-less navigation strategies. The first two approaches were favored in the past. However, they essentially depend on the accurate mapping of the environment and a careful human-guided training phase, which overall limits generalizability. With recent developments in DRL, mapless navigation experienced major advancements. A current challenge for DRL algorithms is learning new tasks or goals. This ability is called transfer learning. To cope with the challenges in transfer learning and performance, we present a new approach using Universal Successor Features (USF) in this thesis. We propose several models that we applied for the task of target-driven visual navigation in a complex photo-realistic environment using a simulator named AI2THOR. With the evaluation of our proposed models in AI2THOR, we demonstrate that an agent is able to successfully improve the ability to reach goals that the agent was initially not trained for.
Robust Network Architecture Search via Feature Distortion Restraining
Authors: Yaguan Qian, Shenghui Huang, Bin Wang, Xiang Ling, Xiaohui Guan, Zhaoquan Gu, Shaoning Zeng, WuJie Zhou, Haijiang Wang
Date: 2022-07-13 00:00:00
Proceeding: ECCV 2022 7
Tasks: Bilevel Optimization

The vulnerability of Deep Neural Networks, i.e., susceptibility to adversarial attacks, severely limits the application of DNNs in security-sensitive domains. Most of existing methods improve model robustness from weight optimization, such as adversarial training. However, the architecture of DNNs is also a key factor to robustness, which is often neglected or underestimated. We propose Robust Network Architecture Search (RNAS) to obtain a robust network against adversarial attacks. We observe that an adversarial perturbation distorting the non-robust features in latent feature space can further aggravate misclassification. Based on this observation, we search the robust architecture through restricting feature distortion in the search process. Specifically, we define a network vulnerability metric based on feature distortion as a constraint in the search process. This process is modeled as a multi-objective bilevel optimization problem and a novel algorithm is proposed to solve this optimization. Extensive experiments conducted on CIFAR-10/100 and SVHN show that RNAS achieves the best robustness under various adversarial attacks compared with extensive baselines and SOTA methods.
PAL: Program-aided Language Models
Authors: Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, PengFei Liu, Yiming Yang, Jamie Callan, Graham Neubig
Date: 2022-11-18 00:00:00
Tasks: Arithmetic Reasoning, GSM8K, Math, Mathematical Reasoning, Math Word Problem Solving

Large language models (LLMs) have recently demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time ("few-shot prompting"). Much of this success can be attributed to prompting methods such as "chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem. While LLMs seem to be adept at this sort of step-by-step decomposition, LLMs often make logical and arithmetic mistakes in the solution part, even when the problem is decomposed correctly. In this paper, we present Program-Aided Language models (PAL): a novel approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a runtime such as a Python interpreter. With PAL, decomposing the natural language problem into runnable steps remains the only learning task for the LLM, while solving is delegated to the interpreter. We demonstrate this synergy between a neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all these natural language reasoning tasks, generating code using an LLM and reasoning using a Python interpreter leads to more accurate results than much larger models. For example, PAL using Codex achieves state-of-the-art few-shot accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B which uses chain-of-thought by absolute 15% top-1. Our code and data are publicly available at http://reasonwithpal.com/ .
Indexing AI Risks with Incidents, Issues, and Variants
Authors: Sean McGregor, Kevin Paeth, Khoa Lam
Date: 2022-11-18 00:00:00
Tasks: Computer Security

Two years after publicly launching the AI Incident Database (AIID) as a collection of harms or near harms produced by AI in the world, a backlog of "issues" that do not meet its incident ingestion criteria have accumulated in its review queue. Despite not passing the database's current criteria for incidents, these issues advance human understanding of where AI presents the potential for harm. Similar to databases in aviation and computer security, the AIID proposes to adopt a two-tiered system for indexing AI incidents (i.e., a harm or near harm event) and issues (i.e., a risk of a harm event). Further, as some machine learning-based systems will sometimes produce a large number of incidents, the notion of an incident "variant" is introduced. These proposed changes mark the transition of the AIID to a new version in response to lessons learned from editing 2,000+ incident reports and additional reports that fall under the new category of "issue."
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction
Authors: Montgomery Flora, Corey Potvin, Amy McGovern, Shawn Handler
Date: 2022-11-18 00:00:00
Tasks: Dimensionality Reduction, Feature Importance

Machine learning (ML) models are becoming increasingly common in the atmospheric science community with a wide range of applications. To enable users to understand what an ML model has learned, ML explainability has become a field of active research. In Part I of this two-part study, we described several explainability methods and demonstrated that feature rankings from different methods can substantially disagree with each other. It is unclear, though, whether the disagreement is overinflated due to some methods being less faithful in assigning importance. Herein, "faithfulness" or "fidelity" refer to the correspondence between the assigned feature importance and the contribution of the feature to model performance. In the present study, we evaluate the faithfulness of feature ranking methods using multiple methods. Given the sensitivity of explanation methods to feature correlations, we also quantify how much explainability faithfulness improves after correlated features are limited. Before dimensionality reduction, the feature relevance methods [e.g., SHAP, LIME, ALE variance, and logistic regression (LR) coefficients] were generally more faithful than the permutation importance methods due to the negative impact of correlated features. Once correlated features were reduced, traditional permutation importance became the most faithful method. In addition, the ranking uncertainty (i.e., the spread in rank assigned to a feature by the different ranking methods) was reduced by a factor of 2-10, and excluding less faithful feature ranking methods reduces it further. This study is one of the first to quantify the improvement in explainability from limiting correlated features and knowing the relative fidelity of different explainability methods.
Always Valid Risk Monitoring for Online Matrix Completion
Authors: Chi-Hua Wang, Wenjie Li
Date: 2022-11-18 00:00:00
Tasks: Matrix Completion, valid

Always-valid concentration inequalities are increasingly used as performance measures for online statistical learning, notably in the learning of generative models and supervised learning. Such inequality advances the online learning algorithms design by allowing random, adaptively chosen sample sizes instead of a fixed pre-specified size in offline statistical learning. However, establishing such an always-valid type result for the task of matrix completion is challenging and far from understood in the literature. Due to the importance of such type of result, this work establishes and devises the always-valid risk bound process for online matrix completion problems. Such theoretical advances are made possible by a novel combination of non-asymptotic martingale concentration and regularized low-rank matrix regression. Our result enables a more sample-efficient online algorithm design and serves as a foundation to evaluate online experiment policies on the task of online matrix completion.