Paper revisits FRAME model, explaining instability and proposing a new metric.
problem Unstable training energy in FRAME model.
method Theoretical analysis using particle physics, proposing a new Wasserstein distance.
result Proposed Wasserstein distance stabilizes energy dissipation and maintains statistical consistency.
ControlVAE improves VAE performance by adding a controller to tune hyperparameters.
problem Existing VAE models struggle with KL vanishing and low reconstruction quality.
method ControlVAE combines a controller inspired by automatic control theory with VAE to improve performance.
result ControlVAE achieves better disentangling and reconstruction quality than existing methods.
Proposes SLCVAE to improve text diversity by self-labeling.
problem KL-Vanishing problem in CVAE for diverse text generation.
method Explicit optimizing objective to guide encoder towards best encoder, using a labeling network.
result Improves text diversity while maintaining comparable accuracy.
Cyclical Annealing Schedule improves VAE performance in NLP tasks.
problem KL term vanishing in VAEs with auto-regressive decoders.
method Cyclical Annealing Schedule to vary β over multiple cycles.
result Progressive learning of meaningful latent codes improves VAE performance.
SGRNN models evolving graph data for better property prediction.
problem Modeling evolving graph data for property prediction.
method SGRNN uses stochastic latent variables to capture both node attribute and topology evolution, with semi-implicit variational inference and KL-divergence simplification.
result SGRNN improves property prediction on real-world datasets.
TFCheck detects training issues in ML programs using TensorFlow.
problem Training programs often contain inconsistencies and bugs.
method Developed a TensorFlow library (TFCheck) with verification routines.
result TFCheck successfully detects training issues in ML code.
Investigates numerical issues in GP interpolation parameter estimation.
problem Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation.
method Investigates and proposes strategies to improve open-source software implementations.
result Improves reliability and reproducibility of studies relying on GP implementations.
Paper improves volatility forecasting for new issues and spin-offs.
problem Forecasting volatility with limited historical data.
method Multi-source transfer learning approach.
result Transfer learning approach outperforms alternative models.
We develop a model of issue-specific voting behavior. This model can be used to explore lawmakers' personal voting patterns of voting by issue area, providing an exploratory window into how the language of the law is correlated with political support. We derive approximate posterior inference algorithms based on variat…
Machine learning conferences face ethical issues in review process.
problem Ethical issues in the review process of machine learning conferences.
method Study of recruitment issues, double-blind process infringements, fraudulent behaviors, biases, and appendix phenomenon.
result Highlighting the need for awareness in the machine learning community.
SIGMA issues a special birthday tribute to Fuchs.
problem N/A
method N/A
result N/A
Special issue on understanding physical processes from unusual diffusion patterns.
problem Understanding physical processes from anomalous diffusion data.
method Not explicitly described in the abstract, but likely involves analysis of data from the Anomalous Diffusion Challenge.
result Not explicitly stated, but likely includes analysis of physical processes from anomalous diffusion data.
The paper tackles three financial issues: time resolution, nonstationarity, and latent factors.
problem Three fundamental issues in financial data: time resolution, nonstationarity, and latent factors.
method A causal perspective to reexamine and solve these issues.
result Provides systematic solutions to financial data issues.
Discussing issues in robust clustering, especially with Gaussian models.
problem Handling outliers and ambiguity in clustering groups.
method Focus on Gaussian mixture model, examining formal definitions, interactions, and tuning decisions.
result Outliers can confuse clustering groups and existing stability measures fail with them.
We discuss Russia's underlying motives for issuing its government-backed cryptocurrency, CryptoRuble, and the implications thereof and of other likely-soon-forthcoming government-issued cryptocurrencies to some stakeholders (populace, governments, economy, finance, etc.), existing decentralized cryptocurrencies (such a…
KL-constrained API shows optimization issues and improved with regularization.
problem Optimization issues in KL-constrained API algorithms.
method Comparison of KL divergence as a constraint vs. regularizer, empirical evaluation.
result KL-constrained API is not guaranteed to converge and incurs linear regret.
Survey on reproducibility and distortion issues in text clustering and topic modeling.
problem Reproducibility and misleading cluster geometry in unsupervised learning for text categorization.
method Systematic literature review of text clustering and topic modeling from 2011-2022.
result Outliers and initialization issues are significant factors in text clustering and topic modeling.
Online trading platforms manipulate profits and losses, causing 82% of retail traders to lose money.
problem Manipulation of online trading platforms leading to financial losses for retail traders.
method Independent recording of trade details using REST API responses, comparison with broker reviews.
result 82% of retail traders lose money due to platform technical issues.
Special issue honors Stanley Deser, focusing on advanced physics topics.
problem None explicitly stated in the abstract.
method Collection of articles in memory of Stanley Deser.
result No specific key result mentioned in the abstract.
The paper tackles scalarization issues in A2C RL algorithms, proposing methods to avoid gradient overlap and noise.
problem Scalarization issues in A2C RL algorithms leading to gradient overlap and uncontrolled noise.
method Proposes techniques to avoid gradient overlap and noise in A2C RL algorithms.
result Pilot experiments show the proposed method speeds up training in A2C RL algorithms.
PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use as an anomaly detection technique. However, it is limited in this regard by being sensitive to gross perturbations of the input, and by seeking a linear subspace that captures normal behaviour. The first issue has bee…
Paper identifies objective mismatch in MBRL, affecting control task performance.
problem Objective mismatch in MBRL framework affects control task performance.
method Proposes re-weighting dynamics model training to mitigate mismatch.
result Likelihood of one-step ahead predictions is not always correlated with control performance.
This is a survey of some of the work of Tom Farrell and Lowell Jones. This is the lead article of a special issue of the Pure and Applied Mathematics Quarterly. This issue is published in conjunction with the conference "Geometry,Topology, and their Interactions" held in Morelia, Mexico.
New issue found in value-based reinforcement learning for stochastic environments.
problem Value-based reinforcement learning struggles with stochastic state transitions.
method Demonstrated using a multiobjective Markov Decision Process (MOMDP).
result Approaches may converge to Pareto-dominated solutions instead of optimal ones.
NAS best practices guide reduces evaluation issues.
problem Lack of scientific evaluation quality in NAS.
method Described NAS best practices and checklist.
result Reduces evaluation issues in NAS.
GANs have hidden assumptions causing practical issues.
problem GANs' practical performance contradicts theoretical guarantees.
method Proposed an alternative method to avoid these issues.
result Alternative method sidesteps the identified problems.
DGPs with variational inference suffer from SNR issues that degrade gradient estimates, leading to unreliable training.
problem SNR issues in gradient estimates for DGPs with variational inference.
method Adapted doubly reparameterized gradient estimators for DGP training.
result Fix improves predictive performance of DGP models.
FDS tackles long horizon hyperparameter optimization issues.
problem Memory scaling and gradient degradation in long horizon tasks.
method Forward-mode differentiation with sharing (FDS).
result Significantly outperforms greedy gradient-based alternatives.
New convergence issues found in AMSGrad and a new version proposed.
problem AMSGrad convergence proof issues and neglected hyper-parameter treatment.
method Provided counter-example and proposed new convergence proof and version.
result AMSGrad convergence proof issues and new version AMSX.
In this editorial guide for the special issue on econophysics, we give a unique review of this young but quickly growing discipline. A suggestive taxonomy of the development is proposed by making a distinction between classical econophysics and modern econophysics. For each of these two stages of development, we identi…
Proposes a new approach to regression learning that addresses overfitting and underfitting.
problem Regression learning issues, including overfitting and underfitting.
method Introduces epsilon-Confidence Approximately Correct (epsilon CoAC) framework using Kullback Leibler divergence.
result Demonstrates improved learnability and accuracy compared to cross-validation.
This paper explores optimization methods for deep learning.
problem How to effectively train neural networks.
method Discusses gradient explosion/vanishing, initialization, normalization, SGD, adaptive gradient methods, distributed methods, and global issues.
result Provides theoretical insights and practical solutions for neural network training.
Proposes DATELINE for aggregating k-ary preferences with uncertainty.
problem Aggregating k-ary preferences with feature information and uncertainty.
method Employing deep neural networks and a weighted Plackett-Luce model with uncertainty vectors.
result Provides theoretical guarantees for robustness.
This paper discusses issues in mining user behavioral rules for context-aware mobile apps.
problem Mining contextual behavioral rules from smartphone data.
method Addressing quality of data, relevancy of contexts, discretization, rule discovery, semantic understanding, and dynamic rule updating.
result Potential solutions for mining user behavioral rules for context-aware mobile apps.
Generative Adversarial Networks (GANs) have become a popular method to learn a probability model from data. In this paper, we aim to provide an understanding of some of the basic issues surrounding GANs including their formulation, generalization and stability on a simple benchmark where the data has a high-dimensional…
Unsupervised deep learning detects image quality issues without annotations.
problem Anomaly detection in images with quality issues.
method Use of deep generative models, specifically normalizing flows, for anomaly detection.
result Accurate semantic labeling and quality trends observed in images.
Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide ne…
This review explores XAI methods and multicollinearity issues.
problem Multicollinearity affects the interpretability of AI models.
method A review of current XAI approaches and their limitations.
result Current XAI methods struggle with multicollinearity.
We address feature interpretation and reproducibility issues in dense nets, proposing a modified loss function.
problem Feature interpretation and reproducibility issues in dense nets.
method Proposed a modified loss function to circumvent basis collapse.
result Substantially concise nets with 100x fewer parameters and lower MSE loss.
GAN+VER improves GANs by regularizing entropy to reduce mode collapse.
problem Mode collapse in GANs where the generator fails to capture all modes.
method Maximizing a variational lower bound on the entropy of generated samples.
result Significant improvement in evaluation metrics for real and generated samples.
High volume of data, perceived as either challenge or opportunity. Deep learning architecture demands high volume of data to effectively back propagate and train the weights without bias. At the same time, large volume of data demands higher capacity of the machine where it could be executed seamlessly. Budding data sc…
Much has been said lately about entrepreneurship, so it seems important to leave here some personal analysis on this topic. The issues outlined here result from a work in about a year in which because a personal and professional obligations it was doing some research on these issues. This is an interesting topic that h…
This work examines consistency issues in Gaussian Mixture Model reduction algorithms.
problem Consistency issues in Gaussian Mixture Model reduction algorithms.
method Discussion of the importance of dissimilarity measure choice and consistency of GMR algorithms.
result Most existing GMR algorithms are not consistent with a unique measure, leading to suboptimal reduced GMs.
This paper tackles security issues in deep reinforcement learning.
problem Security risks and issues in deep reinforcement learning algorithms.
method Formulated security requirements, threat model, and reviewed current literature.
result Identified vulnerabilities, attack vectors, and adversarial capabilities in deep RL.
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding …
AdvReg improves VQA models but introduces instability and bias issues.
problem VQA models over-rely on linguistic biases, ignoring visual context.
method Adversarial regularization to encourage bias-free question representations.
result AdvReg yields side-effects like unstable gradients and reduced performance on in-domain examples.
Multiple Additive Regression Trees (MART), an ensemble model of boosted regression trees, is known to deliver high prediction accuracy for diverse tasks, and it is widely used in practice. However, it suffers an issue which we call over-specialization, wherein trees added at later iterations tend to impact the predicti…
Notions of "fair classification" that have arisen in computer science generally revolve around equalizing certain statistics across protected groups. This approach has been criticized as ignoring societal issues, including how errors can hurt certain groups disproportionately. We pose a modification of one of the fairn…