pwc-archive/papers-with-abstracts huggingface dataset:
The Role of Deep Learning in Financial Asset Management: A Systematic Review
Authors: Pedro Reis, Ana Paula Serra, João Gama
Date: 2025-03-03 00:00:00
Tasks: Articles, Asset Management, Deep Learning, Deep Reinforcement Learning, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Management, Sentiment Analysis

This review systematically examines deep learning applications in financial asset management. Unlike prior reviews, this study focuses on identifying emerging trends, such as the integration of explainable artificial intelligence (XAI) and deep reinforcement learning (DRL), and their transformative potential. It highlights new developments, including hybrid models (e.g., transformer-based architectures) and the growing use of alternative data sources such as ESG indicators and sentiment analysis. These advancements challenge traditional financial paradigms and set the stage for a deeper understanding of the evolving landscape. We use the Scopus database to select the most relevant articles published from 2018 to 2023. The inclusion criteria encompassed articles that explicitly apply deep learning models within financial asset management. We excluded studies focused on physical assets. This review also outlines our methodology for evaluating the relevance and impact of the included studies, including data sources and analytical methods. Our search identified 934 articles, with 612 meeting the inclusion criteria based on their focus and methodology. The synthesis of results from these articles provides insights into the effectiveness of deep learning models in improving portfolio performance and price forecasting accuracy. The review highlights the broad applicability and potential enhancements deep learning offers to financial asset management. Despite some limitations due to the scope of model application and variation in methodological rigour, the overall evidence supports deep learning as a valuable tool in this field. Our systematic review underscores the progressive integration of deep learning in financial asset management, suggesting a trajectory towards more sophisticated and impactful applications.
Cauchy-Schwarz Regularizers
Authors: Sueda Taner, Ziyi Wang, Christoph Studer
Date: 2025-03-03 00:00:00
Tasks: Quantization

We introduce a novel class of regularization functions, called Cauchy-Schwarz (CS) regularizers, which can be designed to induce a wide range of properties in solution vectors of optimization problems. To demonstrate the versatility of CS regularizers, we derive regularization functions that promote discrete-valued vectors, eigenvectors of a given matrix, and orthogonal matrices. The resulting CS regularizers are simple, differentiable, and can be free of spurious stationary points, making them suitable for gradient-based solvers and large-scale optimization problems. In addition, CS regularizers automatically adapt to the appropriate scale, which is, for example, beneficial when discretizing the weights of neural networks. To demonstrate the efficacy of CS regularizers, we provide results for solving underdetermined systems of linear equations and weight quantization in neural networks. Furthermore, we discuss specializations, variations, and generalizations, which lead to an even broader class of new and possibly more powerful regularizers.
\textsc{Perseus}: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes
Authors: Honglin Fu, Yebo Feng, Cong Wu, Jiahua Xu
Date: 2025-03-03 00:00:00
Tasks: Fraud Detection

Masterminds are entities organizing, coordinating, and orchestrating cryptocurrency pump-and-dump schemes, a form of trade-based manipulation undermining market integrity and causing financial losses for unwitting investors. Previous research detects pump-and-dump activities in the market, predicts the target cryptocurrency, and examines investors and \ac{osn} entities. However, these solutions do not address the root cause of the problem. There is a critical gap in identifying and tracing the masterminds involved in these schemes. In this research, we develop a detection system \textsc{Perseus}, which collects real-time data from the \acs{osn} and cryptocurrency markets. \textsc{Perseus} then constructs temporal attributed graphs that preserve the direction of information diffusion and the structure of the community while leveraging \ac{gnn} to identify the masterminds behind pump-and-dump activities. Our design of \textsc{Perseus} leads to higher F1 scores and precision than the \ac{sota} fraud detection method, achieving fast training and inferring speeds. Deployed in the real world from February 16 to October 9 2024, \textsc{Perseus} successfully detects $438$ masterminds who are efficient in the pump-and-dump information diffusion networks. \textsc{Perseus} provides regulators with an explanation of the risks of masterminds and oversight capabilities to mitigate the pump-and-dump schemes of cryptocurrency.
Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyond
Authors: Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
Date: 2025-03-03 00:00:00

Most microeconomic models of interest involve optimizing a piecewise linear function. These include contract design in hidden-action principal-agent problems, selling an item in posted-price auctions, and bidding in first-price auctions. When the relevant model parameters are unknown and determined by some (unknown) probability distributions, the problem becomes learning how to optimize an unknown and stochastic piecewise linear reward function. Such a problem is usually framed within an online learning framework, where the decision-maker (learner) seeks to minimize the regret of not knowing an optimal decision in hindsight. This paper introduces a general online learning framework that offers a unified approach to tackle regret minimization for piecewise linear rewards, under a suitable monotonicity assumption commonly satisfied by microeconomic models. We design a learning algorithm that attains a regret of $\widetilde{O}(\sqrt{nT})$, where $n$ is the number of ``pieces'' of the reward function and $T$ is the number of rounds. This result is tight when $n$ is \emph{small} relative to $T$, specifically when $n \leq T^{1/3}$. Our algorithm solves two open problems in the literature on learning in microeconomic settings. First, it shows that the $\widetilde{O}(T^{2/3})$ regret bound obtained by Zhu et al. [Zhu+23] for learning optimal linear contracts in hidden-action principal-agent problems is not tight when the number of agent's actions is small relative to $T$. Second, our algorithm demonstrates that, in the problem of learning to set prices in posted-price auctions, it is possible to attain suitable (and desirable) instance-independent regret bounds, addressing an open problem posed by Cesa-Bianchi et al. [CBCP19].
m4: A Learned Flow-level Network Simulator
Authors: Chenning Li, Anton A. Zabreyko, Arash Nasr-Esfahany, Kevin Zhao, Prateesh Goyal, Mohammad Alizadeh, Thomas Anderson
Date: 2025-03-03 00:00:00

Flow-level simulation is widely used to model large-scale data center networks due to its scalability. Unlike packet-level simulators that model individual packets, flow-level simulators abstract traffic as continuous flows with dynamically assigned transmission rates. While this abstraction enables orders-of-magnitude speedup, it is inaccurate by omitting critical packet-level effects such as queuing, congestion control, and retransmissions. We present m4, an accurate and scalable flow-level simulator that uses machine learning to learn the dynamics of the network of interest. At the core of m4 lies a novel ML architecture that decomposes state transition computations into distinct spatial and temporal components, each represented by a suitable neural network. To efficiently learn the underlying flow-level dynamics, m4 adds dense supervision signals by predicting intermediate network metrics such as remaining flow size and queue length during training. m4 achieves a speedup of up to 104$\times$ over packet-level simulation. Relative to a traditional flow-level simulation, m4 reduces per-flow estimation errors by 45.3% (mean) and 53.0% (p90). For closed-loop applications, m4 accurately predicts network throughput under various congestion control schemes and workloads.
PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier
Authors: Md. Farhan Shahriyar, Gazi Tanbhir, Abdullah Md Raihan Chy, Mohammed Abdul Al Arafat Tanzin, Md. Jisan Mashrafi
Date: 2025-03-03 00:00:00
Tasks: feature selection, Quantum Machine Learning

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive infor mation. Traditional machine learning techniques struggle to per form well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and vari ational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows.
FLEXtime: Filterbank learning to explain time series
Authors: Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Robert Jenssen, Tommy S. Alstrøm
Date: 2024-11-06 00:00:00
Tasks: EEG, Time Series

State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are difficult to interpret in the time domain, due to the inherently complex nature of the data. Instead, we propose to view time series explainability as saliency maps over interpretable parts, leaning on established signal processing methodology on signal decomposition. Specifically, we propose a new method called FLEXtime that uses a bank of bandpass filters to split the time series into frequency bands. Then, we learn the combination of these bands that optimally explains the model's prediction. Our extensive evaluation shows that, on average, FLEXtime outperforms state-of-the-art explainability methods across a range of datasets. FLEXtime fills an important gap in the current time series explainability methodology and is a valuable tool for a wide range of time series such as EEG and audio. Code will be made available at https://github.com/theabrusch/FLEXtime.
Direct Distributional Optimization for Provable Alignment of Diffusion Models
Authors: Ryotaro Kawata, Kazusato Oko, Atsushi Nitanda, Taiji Suzuki
Date: 2025-02-05 00:00:00

We introduce a novel alignment method for diffusion models from distribution optimization perspectives while providing rigorous convergence guarantees. We first formulate the problem as a generic regularized loss minimization over probability distributions and directly optimize the distribution using the Dual Averaging method. Next, we enable sampling from the learned distribution by approximating its score function via Doob's $h$-transform technique. The proposed framework is supported by rigorous convergence guarantees and an end-to-end bound on the sampling error, which imply that when the original distribution's score is known accurately, the complexity of sampling from shifted distributions is independent of isoperimetric conditions. This framework is broadly applicable to general distribution optimization problems, including alignment tasks in Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), and Kahneman-Tversky Optimization (KTO). We empirically validate its performance on synthetic and image datasets using the DPO objective.
$μ$nit Scaling: Simple and Scalable FP8 LLM Training
Authors: Saaketh Narayan, Abhay Gupta, Mansheej Paul, Davis Blalock
Date: 2025-02-09 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large Language Model training with 8-bit floating point (FP8) formats promises significant efficiency improvements, but reduced numerical precision makes training challenging. It is currently possible to train in FP8 only if one is willing to tune various hyperparameters, reduce model scale, or accept the overhead of computing dynamic scale factors. We demonstrate simple, scalable FP8 training that requires no dynamic scaling factors or special hyperparameters, even at large model sizes. Our method, $\mu$nit Scaling ($\mu$S), also enables simple hyperparameter transfer across model widths, matched numerics across training and inference, and other desirable properties. $\mu$nit Scaling is straightforward to implement, consisting of a set of minimal interventions based on a first-principles analysis of common transformer operations. We validate our method by training models from 1B to 13B parameters, performing all hidden linear layer computations in FP8. We achieve quality equal to higher precision baselines while also training up to 33% faster.
Modern Hopfield Networks with Continuous-Time Memories
Authors: Saul Santos, António Farinhas, Daniel C. McNamee, André F. T. Martins
Date: 2025-02-14 00:00:00

Recent research has established a connection between modern Hopfield networks (HNs) and transformer attention heads, with guarantees of exponential storage capacity. However, these models still face challenges scaling storage efficiently. Inspired by psychological theories of continuous neural resource allocation in working memory, we propose an approach that compresses large discrete Hopfield memories into smaller, continuous-time memories. Leveraging continuous attention, our new energy function modifies the update rule of HNs, replacing the traditional softmax-based probability mass function with a probability density, over the continuous memory. This formulation aligns with modern perspectives on human executive function, offering a principled link between attractor dynamics in working memory and resource-efficient memory allocation. Our framework maintains competitive performance with HNs while leveraging a compressed memory, reducing computational costs across synthetic and video datasets.
Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination
Authors: Weilin Chen, Ruichu Cai, Junjie Wan, Zeqin Yang, José Miguel Hernández-Lobato
Date: 2025-02-26 00:00:00
Tasks: Causal Inference

Long-term causal inference has drawn increasing attention in many scientific domains. Existing methods mainly focus on estimating average long-term causal effects by combining long-term observational data and short-term experimental data. However, it is still understudied how to robustly and effectively estimate heterogeneous long-term causal effects, significantly limiting practical applications. In this paper, we propose several two-stage style nonparametric estimators for heterogeneous long-term causal effect estimation, including propensity-based, regression-based, and multiple robust estimators. We conduct a comprehensive theoretical analysis of their asymptotic properties under mild assumptions, with the ultimate goal of building a better understanding of the conditions under which some estimators can be expected to perform better. Extensive experiments across several semi-synthetic and real-world datasets validate the theoretical results and demonstrate the effectiveness of the proposed estimators.
Using High-Level Patterns to Estimate How Humans Predict a Robot will Behave
Authors: Sagar Parekh, Lauren Bramblett, Nicola Bezzo, Dylan P. Losey
Date: 2024-09-20 00:00:00

Humans interacting with robots often form predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might predict that the car is going to remain in the same lane. It is important for the robot to understand the human's prediction for safe and seamless interaction: e.g., if the autonomous car knows the human thinks it is not merging -- but the autonomous car actually intends to merge -- then the car can adjust its behavior to prevent an accident. Prior works typically assume that humans make precise predictions of robot behavior. However, recent research on human-human prediction suggests the opposite: humans tend to approximate other agents by predicting their high-level behaviors. We apply this finding to develop a second-order theory of mind approach that enables robots to estimate how humans predict they will behave. To extract these high-level predictions directly from data, we embed the recent human and robot trajectories into a discrete latent space. Each element of this latent space captures a different type of behavior (e.g., merging in front of the human, remaining in the same lane) and decodes into a vector field across the state space that is consistent with the underlying behavior type. We hypothesize that our resulting high-level and course predictions of robot behavior will correspond to actual human predictions. We provide initial evidence in support of this hypothesis through proof-of-concept simulations, testing our method's predictions against those of real users, and experiments on a real-world interactive driving dataset.
The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data
Authors: Evgenii Evstafev
Date: 2025-02-12 00:00:00
Tasks: Computational Efficiency, Diversity, Model Selection

This study examines how temperature settings and model architectures affect the generation of structured fictional data (names, birthdates) across three large language models (LLMs): llama3.1:8b, deepseek-r1:8b, and mistral:latest. By systematically testing temperature values from 0.0 to 1.0 in increments of 0.1, we conducted 330 trials yielding 889 structured entities, validated for syntactic consistency. Key findings reveal that model architecture significantly influences computational efficiency, with mistral:latest and llama3.1:8b processing data 8x faster than deepseek-r1:8b. Contrary to expectations, temperature showed no correlation with processing time, challenging assumptions about stochastic sampling costs. Output diversity remained limited, as models consistently defaulted to common name archetypes (e.g., 'John Doe' and 'Jane Smith') across all temperatures, though rare names clustered at intermediate values (0.3-0.7). These results demonstrate that architectural optimizations, rather than temperature adjustments, dominate performance in structured generation tasks. The findings emphasize prioritizing model selection over hyperparameter tuning for efficiency and suggest explicit diversity constraints are necessary to mitigate default output biases in synthetic data pipelines.
Water Bottle Defect Detection System Using Convolutional Neural Network
Authors: Asaminew Gizaw, Tegegn Kebebaw
Date: 2022-12-08 00:00:00
Proceeding: 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA) 2022 12
Tasks: Data Augmentation, Defect Detection, object-detection, Object Detection

Water bottling is the major task in water
and beverage production companies. One of the most
concerning issues during the bottling process is to
assure Encapsulation quality, such as no cover, high
cover, crooked (curved) cover, this kind of issues
could bring serious food safety problems. Therefore,
it is necessary to detect these defects in the process of
production and take out the unqualified bottles from
production lines. In this study we presented the
presented a mechanism to detect defects during
bottling process using convolutional neural network
algorithm. We collected water bottle images from
different sources and categorized the collected images
as the training, validation, and testing data, which
contains 7800, 1800, and 184 images respectively. All
categories have been labeled as: broken, normal
water, normal cap, no cap, foreign object and
scratched. After data organization, we have applied
different image preprocessing and data
augmentation. The processed data was used to train a
developed convolutional neural network model.
Additionally, you only look once version 3 (YOLO3)
object detection algorithm was used to detect bottles
from the input image. After all, the accuracy of the
developed model is compared with the accuracy of
the pre-trained VGG16 and ResNet50 model by
training them on our dataset. In our study we are
able to gate 96% of training accuracy and 99% of
validation accuracy.
Surface Defects Detection of Transparent Plastic Bottles Based on Improved Yolov5
Authors: Jingni Pei, Shujuan Li, Yan Li
Date: 2023-07-29 00:00:00
Proceeding: http://www.elsevier.com/locate/imavis 2023 7
Tasks: object-detection, Object Detection, Object Recognition

Given the high demand for transparent plastic bottles in our daily lives, coupled with their appearance being a crucial quality indicator, it is imperative to pay close attention to detecting surface defects on these bottles.Traditional manual detection methods are inefficient, time-consuming, labor-intensive, and costly. A deep learning technique is proposed in this work to perform automatic transparent plastic bottles surface defects detection by improving a YOLOv5 object detection algorithm. To enhance the precision of small target detection in YOLOv5, we propose a more friendly pyramid pooling model for small target(SOSPP). This model replaces the pooling operation with dilated convolutions featuring different expansion rates and adds a small target branch.At the same time,it incorporates an ECA and CBMA attention mechanism to improve object recognition for small objects. Moreover, the multi-scale pyramid channel spatial fusion attention mechanism(PCSA) has been incorporated into the feature extraction stage to enhance the detection of small targets.The effectiveness is evaluated based on our exclusive dataset, and the outcomes indicate that the enhanced YOLOv5 significantly improves its ability to detect small target defects.
Reinforced Lifelong Editing for Language Models
Authors: Zherui Li, Houcheng Jiang, Hao Chen, Baolong Bi, Zhenhong Zhou, Fei Sun, Junfeng Fang, Xiang Wang
Date: 2025-02-09 00:00:00
Tasks: Model Editing

Large language models (LLMs) acquire information from pre-training corpora, but their stored knowledge can become inaccurate or outdated over time. Model editing addresses this challenge by modifying model parameters without retraining, and prevalent approaches leverage hypernetworks to generate these parameter updates. However, they face significant challenges in lifelong editing due to their incompatibility with LLM parameters that dynamically change during the editing process. To address this, we observed that hypernetwork-based lifelong editing aligns with reinforcement learning modeling and proposed RLEdit, an RL-based editing method. By treating editing losses as rewards and optimizing hypernetwork parameters at the full knowledge sequence level, we enable it to precisely capture LLM changes and generate appropriate parameter updates. Our extensive empirical evaluation across several LLMs demonstrates that RLEdit outperforms existing methods in lifelong editing with superior effectiveness and efficiency, achieving a 59.24% improvement while requiring only 2.11% of the time compared to most approaches. Our code is available at: https://github.com/zhrli324/RLEdit.