pwc-archive/papers-with-abstracts huggingface dataset:
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection
Authors: Rheeya Uppaal, Junjie Hu, Yixuan Li
Date: 2023-05-22 00:00:00
Tasks: Out of Distribution (OOD) Detection

Out-of-distribution (OOD) detection is a critical task for reliable predictions over text. Fine-tuning with pre-trained language models has been a de facto procedure to derive OOD detectors with respect to in-distribution (ID) data. Despite its common use, the understanding of the role of fine-tuning and its necessity for OOD detection is largely unexplored. In this paper, we raise the question: is fine-tuning necessary for OOD detection? We present a study investigating the efficacy of directly leveraging pre-trained language models for OOD detection, without any model fine-tuning on the ID data. We compare the approach with several competitive fine-tuning objectives, and offer new insights under various types of distributional shifts. Extensive evaluations on 8 diverse ID-OOD dataset pairs demonstrate near-perfect OOD detection performance (with 0% FPR95 in many cases), strongly outperforming its fine-tuned counterparts. We show that using distance-based detection methods, pre-trained language models are near-perfect OOD detectors when the distribution shift involves a domain change. Furthermore, we study the effect of fine-tuning on OOD detection and identify how to balance ID accuracy with OOD detection performance. Our code is publically available at https://github.com/Uppaal/lm-ood.
Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection
Authors: Mithun Das, Saurabh Kumar Pandey, Animesh Mukherjee
Date: 2023-05-22 00:00:00
Tasks: Hate Speech Detection

Hate speech is a severe issue that affects many online platforms. So far, several studies have been performed to develop robust hate speech detection systems. Large language models like ChatGPT have recently shown a great promise in performing several tasks, including hate speech detection. However, it is crucial to comprehend the limitations of these models to build robust hate speech detection systems. To bridge this gap, our study aims to evaluate the strengths and weaknesses of the ChatGPT model in detecting hate speech at a granular level across 11 languages. Our evaluation employs a series of functionality tests that reveals various intricate failures of the model which the aggregate metrics like macro F1 or accuracy are not able to unfold. In addition, we investigate the influence of complex emotions, such as the use of emojis in hate speech, on the performance of the ChatGPT model. Our analysis highlights the shortcomings of the generative models in detecting certain types of hate speech and highlighting the need for further research and improvements in the workings of these models.
A Machine Learning Approach to Detect Dehydration in Afghan Children
Authors: Ziaullah Momand, Debajyoti Pal, Pornchai Mongkolnam, Jonathan H. Chan
Date: 2023-05-22 00:00:00

Child dehydration is a significant health concern, especially among children under 5 years of age who are more susceptible to diarrhea and vomiting. In Afghanistan, severe diarrhea contributes to child mortality due to dehydration. However, there is no evidence of research exploring the potential of machine learning techniques in diagnosing dehydration in Afghan children under five. To fill this gap, this study leveraged various classifiers such as Random Forest, Multilayer Perceptron, Support Vector Machine, J48, and Logistic Regression to develop a predictive model using a dataset of sick children retrieved from the Afghanistan Demographic and Health Survey (ADHS). The primary objective was to determine the dehydration status of children under 5 years. Among all the classifiers, Random Forest proved to be the most effective, achieving an accuracy of 91.46%, precision of 91%, and AUC of 94%. This model can potentially assist healthcare professionals in promptly and accurately identifying dehydration in under five children, leading to timely interventions, and reducing the risk of severe health complications. Our study demonstrates the potential of machine learning techniques in improving the early diagnosis of dehydration in Afghan children.
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks
Authors: Charles Arnal, Felix Hensel, Mathieu Carrière, Théo Lacombe, Hiroaki Kurihara, Yuichi Ike, Frédéric Chazal
Date: 2023-05-22 00:00:00

Despite their successful application to a variety of tasks, neural networks remain limited, like other machine learning methods, by their sensitivity to shifts in the data: their performance can be severely impacted by differences in distribution between the data on which they were trained and that on which they are deployed. In this article, we propose a new family of representations, called MAGDiff, that we extract from any given neural network classifier and that allows for efficient covariate data shift detection without the need to train a new model dedicated to this task. These representations are computed by comparing the activation graphs of the neural network for samples belonging to the training distribution and to the target distribution, and yield powerful data- and task-adapted statistics for the two-sample tests commonly used for data set shift detection. We demonstrate this empirically by measuring the statistical powers of two-sample Kolmogorov-Smirnov (KS) tests on several different data sets and shift types, and showing that our novel representations induce significant improvements over a state-of-the-art baseline relying on the network output.
Modulation Extraction for LFO-driven Audio Effects
Authors: Christopher Mitcheltree, Christian J. Steinmetz, Marco Comunità, Joshua D. Reiss
Date: 2023-05-22 00:00:00

Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus, modify an input signal using time-varying filters and delays, resulting in characteristic sweeping or widening effects. It has been shown that these effects can be modeled using neural networks when conditioned with the ground truth LFO signal. However, in most cases, the LFO signal is not accessible and measurement from the audio signal is nontrivial, hindering the modeling process. To address this, we propose a framework capable of extracting arbitrary LFO signals from processed audio across multiple digital audio effects, parameter settings, and instrument configurations. Since our system imposes no restrictions on the LFO signal shape, we demonstrate its ability to extract quasiperiodic, combined, and distorted modulation signals that are relevant to effect modeling. Furthermore, we show how coupling the extraction model with a simple processing network enables training of end-to-end black-box models of unseen analog or digital LFO-driven audio effects using only dry and wet audio pairs, overcoming the need to access the audio effect or internal LFO signal. We make our code available and provide the trained audio effect models in a real-time VST plugin.
Copy Recurrent Neural Network Structure Network
Authors: Xiaofan Zhou, Xunzhu Tang
Date: 2023-05-22 00:00:00
Tasks: Diagnostic, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Electronic Health Record (EHR) coding involves automatically classifying EHRs into diagnostic codes. While most previous research treats this as a multi-label classification task, generating probabilities for each code and selecting those above a certain threshold as labels, these approaches often overlook the challenge of identifying complex diseases. In this study, our focus is on detecting complication diseases within EHRs. We propose a novel coarse-to-fine ICD path generation framework called the Copy Recurrent Neural Network Structure Network (CRNNet), which employs a Path Generator (PG) and a Path Discriminator (PD) for EHR coding. By using RNNs to generate sequential outputs and incorporating a copy module, we efficiently identify complication diseases. Our method achieves a 57.30\% ratio of complex diseases in predictions, outperforming state-of-the-art and previous approaches. Additionally, through an ablation study, we demonstrate that the copy mechanism plays a crucial role in detecting complex diseases.
Bayesian Numerical Integration with Neural Networks
Authors: Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol
Date: 2023-05-22 00:00:00
Tasks: Numerical Integration

Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the integrand, and can quantify uncertainty over estimates of an integral. However, the most popular algorithm in this class, Bayesian quadrature, is based on Gaussian process models and is therefore associated with a high computational cost. To improve scalability, we propose an alternative approach based on Bayesian neural networks which we call Bayesian Stein networks. The key ingredients are a neural network architecture based on Stein operators, and an approximation of the Bayesian posterior based on the Laplace approximation. We show that this leads to orders of magnitude speed-ups on the popular Genz functions benchmark, and on challenging problems arising in the Bayesian analysis of dynamical systems, and the prediction of energy production for a large-scale wind farm.
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Authors: Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, Sumit Sanghai
Date: 2023-05-22 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling

Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up decoder inference. However, MQA can lead to quality degradation, and moreover it may not be desirable to train a separate model just for faster inference. We (1) propose a recipe for uptraining existing multi-head language model checkpoints into models with MQA using 5% of original pre-training compute, and (2) introduce grouped-query attention (GQA), a generalization of multi-query attention which uses an intermediate (more than one, less than number of query heads) number of key-value heads. We show that uptrained GQA achieves quality close to multi-head attention with comparable speed to MQA.
Chip-Chat: Challenges and Opportunities in Conversational Hardware Design
Authors: Jason Blocklove, Siddharth Garg, Ramesh Karri, Hammond Pearce
Date: 2023-05-22 00:00:00

Modern hardware design starts with specifications provided in natural language. These are then translated by hardware engineers into appropriate Hardware Description Languages (HDLs) such as Verilog before synthesizing circuit elements. Automating this translation could reduce sources of human error from the engineering process. But, it is only recently that artificial intelligence (AI) has demonstrated capabilities for machine-based end-to-end design translations. Commercially-available instruction-tuned Large Language Models (LLMs) such as OpenAI's ChatGPT and Google's Bard claim to be able to produce code in a variety of programming languages; but studies examining them for hardware are still lacking. In this work, we thus explore the challenges faced and opportunities presented when leveraging these recent advances in LLMs for hardware design. Given that these `conversational' LLMs perform best when used interactively, we perform a case study where a hardware engineer co-architects a novel 8-bit accumulator-based microprocessor architecture with the LLM according to real-world hardware constraints. We then sent the processor to tapeout in a Skywater 130nm shuttle, meaning that this `Chip-Chat' resulted in what we believe to be the world's first wholly-AI-written HDL for tapeout.
Estimating Gibbs free energies via isobaric-isothermal flows
Authors: Peter Wirnsberger, Borja Ibarz, George Papamakarios
Date: 2023-05-22 00:00:00

We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal ensemble. In our approach, we approximate the joint distribution of a fully-flexible triclinic simulation box and particle coordinates to achieve a desired internal pressure. This novel extension of flow-based sampling to the isobaric-isothermal ensemble yields direct estimates of Gibbs free energies. We test our NPT-flow on monatomic water in the cubic and hexagonal ice phases and find excellent agreement of Gibbs free energies and other observables compared with established baselines.
Sequential Transfer Learning to Decode Heard and Imagined Timbre from fMRI Data
Authors: Sean Paulsen, Michael Casey
Date: 2023-05-22 00:00:00
Tasks: Transfer Learning

We present a sequential transfer learning framework for transformers on functional Magnetic Resonance Imaging (fMRI) data and demonstrate its significant benefits for decoding musical timbre. In the first of two phases, we pre-train our stacked-encoder transformer architecture on Next Thought Prediction, a self-supervised task of predicting whether or not one sequence of fMRI data follows another. This phase imparts a general understanding of the temporal and spatial dynamics of neural activity, and can be applied to any fMRI dataset. In the second phase, we fine-tune the pre-trained models and train additional fresh models on the supervised task of predicting whether or not two sequences of fMRI data were recorded while listening to the same musical timbre. The fine-tuned models achieve significantly higher accuracy with shorter training times than the fresh models, demonstrating the efficacy of our framework for facilitating transfer learning on fMRI data. Additionally, our fine-tuning task achieves a level of classification granularity beyond standard methods. This work contributes to the growing literature on transformer architectures for sequential transfer learning on fMRI data, and provides evidence that our framework is an improvement over current methods for decoding timbre.
Sequence-to-Sequence Forecasting-aided State Estimation for Power Systems
Authors: Kamal Basulaiman, Masoud Barati
Date: 2023-05-22 00:00:00
Tasks: State Estimation

Power system state forecasting has gained more attention in real-time operations recently. Unique challenges to energy systems are emerging with the massive deployment of renewable energy resources. As a result, power system state forecasting are becoming more crucial for monitoring, operating and securing modern power systems. This paper proposes an end-to-end deep learning framework to accurately predict multi-step power system state estimations in real-time. In our model, we employ a sequence-to-sequence framework to allow for multi-step forecasting. Bidirectional gated recurrent units (BiGRUs) are incorporated into the model to achieve high prediction accuracy. The dominant performance of our model is validated using real dataset. Experimental results show the superiority of our model in predictive power compared to existing alternatives.
Faster Differentially Private Convex Optimization via Second-Order Methods
Date: 2023-05-22 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Second-order methods

Differentially private (stochastic) gradient descent is the workhorse of DP private machine learning in both the convex and non-convex settings. Without privacy constraints, second-order methods, like Newton's method, converge faster than first-order methods like gradient descent. In this work, we investigate the prospect of using the second-order information from the loss function to accelerate DP convex optimization. We first develop a private variant of the regularized cubic Newton method of Nesterov and Polyak, and show that for the class of strongly convex loss functions, our algorithm has quadratic convergence and achieves the optimal excess loss. We then design a practical second-order DP algorithm for the unconstrained logistic regression problem. We theoretically and empirically study the performance of our algorithm. Empirical results show our algorithm consistently achieves the best excess loss compared to other baselines and is 10-40x faster than DP-GD/DP-SGD.
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Authors: Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Date: 2023-05-22 00:00:00
Tasks: regression, Reinforcement Learning (RL)

Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performing practical RL algorithms. However, despite the use of function approximation in practice, the theoretical understanding of MDVI has been limited to tabular Markov decision processes (MDPs). We study MDVI with linear function approximation through its sample complexity required to identify an $\varepsilon$-optimal policy with probability $1-\delta$ under the settings of an infinite-horizon linear MDP, generative model, and G-optimal design. We demonstrate that least-squares regression weighted by the variance of an estimated optimal value function of the next state is crucial to achieving minimax optimality. Based on this observation, we present Variance-Weighted Least-Squares MDVI (VWLS-MDVI), the first theoretical algorithm that achieves nearly minimax optimal sample complexity for infinite-horizon linear MDPs. Furthermore, we propose a practical VWLS algorithm for value-based deep RL, Deep Variance Weighting (DVW). Our experiments demonstrate that DVW improves the performance of popular value-based deep RL algorithms on a set of MinAtar benchmarks.
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning
Authors: Kai Yi, Laurent Condat, Peter Richtárik
Date: 2023-05-22 00:00:00
Tasks: Federated Learning

Federated Learning is an evolving machine learning paradigm, in which multiple clients perform computations based on their individual private data, interspersed by communication with a remote server. A common strategy to curtail communication costs is Local Training, which consists in performing multiple local stochastic gradient descent steps between successive communication rounds. However, the conventional approach to local training overlooks the practical necessity for client-specific personalization, a technique to tailor local models to individual needs. We introduce Scafflix, a novel algorithm that efficiently integrates explicit personalization with local training. This innovative approach benefits from these two techniques, thereby achieving doubly accelerated communication, as we demonstrate both in theory and practice.
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
Authors: Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, Daphne Ippolito
Date: 2023-05-22 00:00:00
Tasks: Decoder

Pretraining is the preliminary and fundamental step in developing capable language models (LM). Despite this, pretraining data design is critically under-documented and often guided by empirically unsupported intuitions. To address this, we pretrain 28 1.5B parameter decoder-only models, training on data curated (1) at different times, (2) with varying toxicity and quality filters, and (3) with different domain compositions. First, we quantify the effect of pretraining data age. A temporal shift between evaluation data and pretraining data leads to performance degradation, which is not overcome by finetuning. Second, we explore the effect of quality and toxicity filters, showing a trade-off between performance on standard benchmarks and risk of toxic generations. Our findings indicate there does not exist a one-size-fits-all solution to filtering training data. We also find that the effects of different types of filtering are not predictable from text domain characteristics. Lastly, we empirically validate that the inclusion of heterogeneous data sources, like books and web, is broadly beneficial and warrants greater prioritization. These findings constitute the largest set of experiments to validate, quantify, and expose many undocumented intuitions about text pretraining, which we hope will help support more informed data-centric decisions in LM development.
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model
Date: 2023-05-22 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Binary Classification

Neural collapse (NC) refers to the surprising structure of the last layer of deep neural networks in the terminal phase of gradient descent training. Recently, an increasing amount of experimental evidence has pointed to the propagation of NC to earlier layers of neural networks. However, while the NC in the last layer is well studied theoretically, much less is known about its multi-layered counterpart - deep neural collapse (DNC). In particular, existing work focuses either on linear layers or only on the last two layers at the price of an extra assumption. Our paper fills this gap by generalizing the established analytical framework for NC - the unconstrained features model - to multiple non-linear layers. Our key technical contribution is to show that, in a deep unconstrained features model, the unique global optimum for binary classification exhibits all the properties typical of DNC. This explains the existing experimental evidence of DNC. We also empirically show that (i) by optimizing deep unconstrained features models via gradient descent, the resulting solution agrees well with our theory, and (ii) trained networks recover the unconstrained features suitable for the occurrence of DNC, thus supporting the validity of this modeling principle.
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search
Authors: Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan, Ramesh Karri, Siddharth Garg
Date: 2023-05-22 00:00:00
Tasks: Reinforcement Learning (RL)

Logic synthesis is the first and most vital step in chip design. This steps converts a chip specification written in a hardware description language (such as Verilog) into an optimized implementation using Boolean logic gates. State-of-the-art logic synthesis algorithms have a large number of logic minimization heuristics, typically applied sequentially based on human experience and intuition. The choice of the order greatly impacts the quality (e.g., area and delay) of the synthesized circuit. In this paper, we propose INVICTUS, a model-based offline reinforcement learning (RL) solution that automatically generates a sequence of logic minimization heuristics ("synthesis recipe") based on a training dataset of previously seen designs. A key challenge is that new designs can range from being very similar to past designs (e.g., adders and multipliers) to being completely novel (e.g., new processor instructions). %Compared to prior work, INVICTUS is the first solution that uses a mix of RL and search methods joint with an online out-of-distribution detector to generate synthesis recipes over a wide range of benchmarks. Our results demonstrate significant improvement in area-delay product (ADP) of synthesized circuits with up to 30\% improvement over state-of-the-art techniques. Moreover, INVICTUS achieves up to $6.3\times$ runtime reduction (iso-ADP) compared to the state-of-the-art.