pwc-archive/papers-with-abstracts huggingface dataset:
Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning
Authors: Anna Lueber, Daniel Kitzmann, Chloe E. Fisher, Brendan P. Bowler, Adam J. Burgasser, Mark Marley, Kevin Heng
Date: 2023-05-12 00:00:00
Tasks: Retrieval

Understanding differences between sub-stellar spectral data and models has proven to be a major challenge, especially for self-consistent model grids that are necessary for a thorough investigation of brown dwarf atmospheres. Using the supervised machine learning method of the random forest, we study the information content of 14 previously published model grids of brown dwarfs (from 1997 to 2021). The random forest method allows us to analyze the predictive power of these model grids, as well as interpret data within the framework of Approximate Bayesian Computation (ABC). Our curated dataset includes 3 benchmark brown dwarfs (Gl 570D, {\epsilon} Indi Ba and Bb) as well as a sample of 19 L and T dwarfs; this sample was previously analyzed in Lueber et al. (2022) using traditional Bayesian methods (nested sampling). We find that the effective temperature of a brown dwarf can be robustly predicted independent of the model grid chosen for the interpretation. However, inference of the surface gravity is model-dependent. Specifically, the BT-Settl, Sonora Bobcat and Sonora Cholla model grids tend to predict logg ~3-4 (cgs units) even after data blueward of 1.2 {\mu}m have been disregarded to mitigate for our incomplete knowledge of the shapes of alkali lines. Two major, longstanding challenges associated with understanding the influence of clouds in brown dwarf atmospheres remain: our inability to model them from first principles and also to robustly validate these models.
Field theory for optimal signal propagation in ResNets
Authors: Kirsten Fischer, David Dahmen, Moritz Helias
Date: 2023-05-12 00:00:00
Tasks: Sensitivity

Residual networks have significantly better trainability and thus performance than feed-forward networks at large depth. Introducing skip connections facilitates signal propagation to deeper layers. In addition, previous works found that adding a scaling parameter for the residual branch further improves generalization performance. While they empirically identified a particularly beneficial range of values for this scaling parameter, the associated performance improvement and its universality across network hyperparameters yet need to be understood. For feed-forward networks, finite-size theories have led to important insights with regard to signal propagation and hyperparameter tuning. We here derive a systematic finite-size field theory for residual networks to study signal propagation and its dependence on the scaling for the residual branch. We derive analytical expressions for the response function, a measure for the network's sensitivity to inputs, and show that for deep networks the empirically found values for the scaling parameter lie within the range of maximal sensitivity. Furthermore, we obtain an analytical expression for the optimal scaling parameter that depends only weakly on other network hyperparameters, such as the weight variance, thereby explaining its universality across hyperparameters. Overall, this work provides a theoretical framework to study ResNets at finite size.
Using Language Models to Detect Alarming Student Responses
Authors: Christopher M. Ormerod, Milan Patel, Harry Wang
Date: 2023-05-12 00:00:00
Tasks: Language Modeling, Language Modelling

This article details the advances made to a system that uses artificial intelligence to identify alarming student responses. This system is built into our assessment platform to assess whether a student's response indicates they are a threat to themselves or others. Such responses may include details concerning threats of violence, severe depression, suicide risks, and descriptions of abuse. Driven by advances in natural language processing, the latest model is a fine-tuned language model trained on a large corpus consisting of student responses and supplementary texts. We demonstrate that the use of a language model delivers a substantial improvement in accuracy over the previous iterations of this system.
Mastering Percolation-like Games with Deep Learning
Authors: Michael M. Danziger, Omkar R. Gojala, Sean P. Cornelius
Date: 2023-05-12 00:00:00
Tasks: Deep Learning, Q-Learning

Though robustness of networks to random attacks has been widely studied, intentional destruction by an intelligent agent is not tractable with previous methods. Here we devise a single-player game on a lattice that mimics the logic of an attacker attempting to destroy a network. The objective of the game is to disable all nodes in the fewest number of steps. We develop a reinforcement learning approach using deep Q-learning that is capable of learning to play this game successfully, and in so doing, to optimally attack a network. Because the learning algorithm is universal, we train agents on different definitions of robustness and compare the learned strategies. We find that superficially similar definitions of robustness induce different strategies in the trained agent, implying that optimally attacking or defending a network is sensitive the particular objective. Our method provides a new approach to understand network robustness, with potential applications to other discrete processes in disordered systems.
Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results
Authors: Lisa Kühnel, Julian Schneider, Ines Perrar, Tim Adams, Fabian Prasser, Ute Nöthlings, Holger Fröhlich, Juliane Fluck
Date: 2023-05-12 00:00:00
Tasks: Descriptive, Nutrition, Synthetic Data Generation

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e. data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case.
ML-Based Teaching Systems: A Conceptual Framework
Authors: Philipp Spitzer, Niklas Kühl, Daniel Heinz, Gerhard Satzger
Date: 2023-05-12 00:00:00
Tasks: Systematic Literature Review, Transfer Learning

As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and to pass it on to novices. While this knowledge transfer has traditionally taken place through personal interaction, it lacks scalability and requires significant resources and time. IT-based teaching systems have addressed this scalability issue, but their development is still tedious and time-consuming. In this work, we investigate the potential of machine learning (ML) models to facilitate knowledge transfer in an organizational context, leading to more cost-effective IT-based teaching systems. Through a systematic literature review, we examine key concepts, themes, and dimensions to better understand and design ML-based teaching systems. To do so, we capture and consolidate the capabilities of ML models in IT-based teaching systems, inductively analyze relevant concepts in this context, and determine their interrelationships. We present our findings in the form of a review of the key concepts, themes, and dimensions to understand and inform on ML-based teaching systems. Building on these results, our work contributes to research on computer-supported cooperative work by conceptualizing how ML-based teaching systems can preserve expert knowledge and facilitate its transfer from SMEs to human novices. In this way, we shed light on this emerging subfield of human-computer interaction and serve to build an interdisciplinary research agenda.
Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression
Authors: Yixin Gao, Runsen Feng, Zongyu Guo, Zhibo Chen
Date: 2023-05-12 00:00:00
Tasks: Image Compression

Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significantly longer decoding times, which hinders the practical applications of neural image codecs. This issue is especially pronounced when employing an effective yet time-consuming autoregressive context model since it would increase entropy decoding time by orders of magnitude. In this paper, unlike most previous works that pursue optimal RD performance while temporally overlooking the coding complexity, we make a systematical investigation on the rate-distortion-complexity (RDC) optimization in neural image compression. By quantifying the decoding complexity as a factor in the optimization goal, we are now able to precisely control the RDC trade-off and then demonstrate how the rate-distortion performance of neural image codecs could adapt to various complexity demands. Going beyond the investigation of RDC optimization, a variable-complexity neural codec is designed to leverage the spatial dependencies adaptively according to industrial demands, which supports fine-grained complexity adjustment by balancing the RDC tradeoff. By implementing this scheme in a powerful base model, we demonstrate the feasibility and flexibility of RDC optimization for neural image codecs.
Masked Audio Text Encoders are Effective Multi-Modal Rescorers
Authors: Jinglun Cai, Monica Sunkara, Xilai Li, Anshu Bhatia, Xiao Pan, Sravan Bodapati
Date: 2023-05-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Contrastive Learning, Domain Generalization, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems. In this work, we propose Masked Audio Text Encoder (MATE), a multi-modal masked language model rescorer which incorporates acoustic representations into the input space of MLM. We adopt contrastive learning for effectively aligning the modalities by learning shared representations. We show that using a multi-modal rescorer is beneficial for domain generalization of the ASR system when target domain data is unavailable. MATE reduces word error rate (WER) by 4%-16% on in-domain, and 3%-7% on out-of-domain datasets, over the text-only baseline. Additionally, with very limited amount of training data (0.8 hours), MATE achieves a WER reduction of 8%-23% over the first-pass baseline.
LatentPINNs: Generative physics-informed neural networks via a latent representation learning
Authors: Mohammad H. Taufik, Tariq Alkhalifah
Date: 2023-05-11 00:00:00
Tasks: Representation Learning

Physics-informed neural networks (PINNs) are promising to replace conventional partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, they are hampered by the relatively slow convergence and the need to perform additional, potentially expensive, training for different PDE parameters. To solve this limitation, we introduce latentPINN, a framework that utilizes latent representations of the PDE parameters as additional (to the coordinates) inputs into PINNs and allows for training over the distribution of these parameters. Motivated by the recent progress on generative models, we promote the use of latent diffusion models to learn compressed latent representations of the PDE parameters distribution and act as input parameters to NN functional solutions. We use a two-stage training scheme in which the first stage, we learn the latent representations for the distribution of PDE parameters. In the second stage, we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters. We test the approach on a class of level set equations given by the nonlinear Eikonal equation. We specifically share results corresponding to three different sets of Eikonal parameters (velocity models). The proposed method performs well on new phase velocity models without the need for any additional training.
Liver Infection Prediction Analysis using Machine Learning to Evaluate Analytical Performance in Neural Networks by Optimization Techniques
Authors: P. Deivendran, S. Selvakanmani, S. Jegadeesan, V. Vinoth Kumar
Date: 2023-05-11 00:00:00

Liver infection is a common disease, which poses a great threat to human health, but there is still able to identify an optimal technique that can be used on large-level screening. This paper deals with ML algorithms using different data sets and predictive analyses. Therefore, machine ML can be utilized in different diseases for integrating a piece of pattern for visualization. This paper deals with various machine learning algorithms on different liver illness datasets to evaluate the analytical performance using different types of parameters and optimization techniques. The selected classification algorithms analyze the difference in results and find out the most excellent categorization models for liver disease. Machine learning optimization is the procedure of modifying hyperparameters in arrange to employ one of the optimization approaches to minimise the cost function. To set the hyperparameter, include a number of Phosphotase,Direct Billirubin, Protiens, Albumin and Albumin Globulin. Since it describes the difference linking the predictable parameter's true importance and the model's prediction, it is crucial to minimise the cost function.
CQE: A Comprehensive Quantity Extractor
Authors: Satya Almasian, Vivian Kazakova, Philip Göldner, Michael Gertz
Date: 2023-05-15 00:00:00
Tasks: Dependency Parsing

Quantities are essential in documents to describe factual information. They are ubiquitous in application domains such as finance, business, medicine, and science in general. Compared to other information extraction approaches, interestingly only a few works exist that describe methods for a proper extraction and representation of quantities in text. In this paper, we present such a comprehensive quantity extraction framework from text data. It efficiently detects combinations of values and units, the behavior of a quantity (e.g., rising or falling), and the concept a quantity is associated with. Our framework makes use of dependency parsing and a dictionary of units, and it provides for a proper normalization and standardization of detected quantities. Using a novel dataset for evaluation, we show that our open source framework outperforms other systems and -- to the best of our knowledge -- is the first to detect concepts associated with identified quantities. The code and data underlying our framework are available at https://github.com/vivkaz/CQE.
Large Language Models are Zero-Shot Rankers for Recommender Systems
Authors: Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, Wayne Xin Zhao
Date: 2023-05-15 00:00:00
Tasks: Recommendation Systems

Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks. Along this line of research, this work aims to investigate the capacity of LLMs that act as the ranking model for recommender systems. We first formalize the recommendation problem as a conditional ranking task, considering sequential interaction histories as conditions and the items retrieved by other candidate generation models as candidates. To solve the ranking task by LLMs, we carefully design the prompting template and conduct extensive experiments on two widely-used datasets. We show that LLMs have promising zero-shot ranking abilities but (1) struggle to perceive the order of historical interactions, and (2) can be biased by popularity or item positions in the prompts. We demonstrate that these issues can be alleviated using specially designed prompting and bootstrapping strategies. Equipped with these insights, zero-shot LLMs can even challenge conventional recommendation models when ranking candidates are retrieved by multiple candidate generators. The code and processed datasets are available at https://github.com/RUCAIBox/LLMRank.
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs
Authors: Afra Feyza Akyürek, Ekin Akyürek, Aman Madaan, Ashwin Kalyan, Peter Clark, Derry Wijaya, Niket Tandon
Date: 2023-05-15 00:00:00
Tasks: reinforcement-learning, Retrieval, text similarity

Despite their unprecedented success, even the largest language models make mistakes. Similar to how humans learn and improve using feedback, previous work proposed providing language models with natural language feedback to guide them in repairing their outputs. Because human-generated critiques are expensive to obtain, researchers have devised learned critique generators in lieu of human critics while assuming one can train downstream models to utilize generated feedback. However, this approach does not apply to black-box or limited access models such as ChatGPT, as they cannot be fine-tuned. Moreover, in the era of large general-purpose language agents, fine-tuning is neither computationally nor spatially efficient as it results in multiple copies of the network. In this work, we introduce RL4F (Reinforcement Learning for Feedback), a multi-agent collaborative framework where the critique generator is trained to maximize end-task performance of GPT-3, a fixed model more than 200 times its size. RL4F produces critiques that help GPT-3 revise its outputs. We study three datasets for action planning, summarization and alphabetization and show relative improvements up to 10% in multiple text similarity metrics over other learned, retrieval-augmented or prompting-based critique generators.
PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India
Authors: Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay B. Cohen, Manish Shrivastava, Barry Haddow
Date: 2023-05-15 00:00:00
Tasks: Cross-Lingual Abstractive Summarization, Multilingual NLP, Text Summarization

This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs. We detail our construction workflow including data acquisition, processing, and quality assurance. Furthermore, we publish benchmarks for monolingual, cross-lingual, and multilingual summarization by fine-tuning, prompting, as well as translate-and-summarize. Experimental results confirm the crucial role of our data in aiding summarization between Indian languages. Our dataset is publicly available and can be freely modified and re-distributed.
Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text
Authors: Hanieh Khorashadizadeh, Nandana Mihindukulasooriya, Sanju Tiwari, Jinghua Groppe, Sven Groppe
Date: 2023-05-15 00:00:00
Tasks: graph construction, In-Context Learning, Knowledge Graphs, Language Modelling, Question Answering, Recommendation Systems, Relation Extraction, Relation Linking

Knowledge graphs can represent information about the real-world using entities and their relations in a structured and semantically rich manner and they enable a variety of downstream applications such as question-answering, recommendation systems, semantic search, and advanced analytics. However, at the moment, building a knowledge graph involves a lot of manual effort and thus hinders their application in some situations and the automation of this process might benefit especially for small organizations. Automatically generating structured knowledge graphs from a large volume of natural language is still a challenging task and the research on sub-tasks such as named entity extraction, relation extraction, entity and relation linking, and knowledge graph construction aims to improve the state of the art of automatic construction and completion of knowledge graphs from text. The recent advancement of foundation models with billions of parameters trained in a self-supervised manner with large volumes of training data that can be adapted to a variety of downstream tasks has helped to demonstrate high performance on a large range of Natural Language Processing (NLP) tasks. In this context, one emerging paradigm is in-context learning where a language model is used as it is with a prompt that provides instructions and some examples to perform a task without changing the parameters of the model using traditional approaches such as fine-tuning. This way, no computing resources are needed for re-training/fine-tuning the models and the engineering effort is minimal. Thus, it would be beneficial to utilize such capabilities for generating knowledge graphs from text.
Measuring Cross-Lingual Transferability of Multilingual Transformers on Sentence Classification
Authors: Zewen Chi, Heyan Huang, Xian-Ling Mao
Date: 2023-05-15 00:00:00
Tasks: Cross-Lingual Transfer, Sentence, Sentence Classification

Recent studies have exhibited remarkable capabilities of pre-trained multilingual Transformers, especially cross-lingual transferability. However, current methods do not measure cross-lingual transferability well, hindering the understanding of multilingual Transformers. In this paper, we propose IGap, a cross-lingual transferability metric for multilingual Transformers on sentence classification tasks. IGap takes training error into consideration, and can also estimate transferability without end-task data. Experimental results show that IGap outperforms baseline metrics for transferability measuring and transfer direction ranking. Besides, we conduct extensive systematic experiments where we compare transferability among various multilingual Transformers, fine-tuning algorithms, and transfer directions. More importantly, our results reveal three findings about cross-lingual transfer, which helps us to better understand multilingual Transformers.