First ABAW 2020 Competition analyzes affective behavior tasks.
problem Automatic analysis of valence-arousal, basic expressions, and action units in real-world scenarios.
method Provided Aff-Wild2 database, described Challenges, evaluation metrics, and top-performing systems.
result Demonstrated the feasibility of automatic affective behavior analysis in real-world settings.
Kaggle competitions offer valuable insights for business forecasting.
problem Lack of attention to Kaggle competitions in academic forecasting studies.
method Review of results from six Kaggle competitions featuring real-life business forecasting tasks.
result Global ensemble models outperform local single models in Kaggle competitions.
RLHC uses multiple critics at different levels to enhance RL performance.
problem Speed up RL learning and increase cumulative rewards in competition tasks.
method Introducing multiple cooperative critics at two levels of a hierarchy to provide global information.
result RLHC outperforms PPO in tennis and soccer competitions.
A method using competitive experience replay enhances learning from sparse rewards.
problem Learning from sparse rewards in reinforcement learning.
method Competitive experience replay method that augments sparse rewards through an exploration competition between agents.
result The method leads to faster convergence and improved task performance.
ICP separates and competes feature representations to learn diverse information.
problem Learning representations with diversified information.
method Information Competing Process (ICP) separates representations into parts with different mutual information constraints, forcing them to learn independently in a competitive environment.
result ICP facilitates obtaining diversified representations with rich information.
A multi-task model tackles citation purpose classification with limited data.
problem Classifying citations based on their purpose is challenging due to limited labeled data and subjectivity.
method Combines linguistic features, TF-IDF, and an LSTM-with-attention model for multi-task learning.
result Improves classification accuracy compared to single-task models.
Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several researc…
Task focuses on fact checking in Q&A forums, improving over baseline systems.
problem Fact checking in community Q&A forums to distinguish factual from opinion.
method Two subtasks: distinguishing factual vs. opinion/advice/socializing, predicting answer truthfulness.
result Improved over baseline systems for both subtasks, but not for Subtask B.
Combines gradient-based and competitive learning for unsupervised feature extraction.
problem Handling input data without supervision and replicating input manifold topology.
method Integrates gradient-based and competitive learning approaches to learn topological structures.
result The dual competitive layer outperforms the vanilla layer in high-dimensional datasets.
Unified online algorithm for both competitive ratio and regret minimization.
problem Achieving both competitive ratio and regret minimization in online algorithms.
method Extending a regret minimization algorithm to handle movement cost and combining it with a base algorithm.
result Unified online algorithm that guarantees both competitive ratio and small regret over any time interval.
The article proposes a dynamic model for a company's life cycle under competitive influence.
problem Modeling a company's life cycle in a competitive environment.
method Utilized Markov model with known action costs and transition probabilities, affected by outside factors.
result Demonstrates the usefulness of the model in determining future actions of a company.
New framework promotes reproducible, domain-agnostic reinforcement learning algorithms.
problem Domain-specific, compute-resource-maximizing, and non-reproducible participant solutions in reinforcement learning competitions.
method Submission retraining, domain randomization, desemantization through domain obfuscation, and compute/environment-sample budget limitation.
result Participant submissions are reproducible, non-specific to the competition environment, and sample/resource efficient.
HMCNAS generates competitive neural architectures without human-defined parameters.
problem Lack of human-defined parameters in Neural Architecture Search.
method Combines Hidden Markov Chains and Bayesian Optimization for autonomous search space generation and competitive model generation.
result HMCNAS generates competitive models in a short time without human-defined parameters.
Improved model accuracy can reduce overall user accuracy in competitive markets.
problem The impact of model competition on overall user accuracy.
method Defined a model of competition for classification tasks and used data representations to study the effect of scale.
result Improving data representation quality can decrease overall predictive accuracy across users (social welfare) in a competitive market.
Regularizes deep multi-task networks to prevent task interference.
problem Interfering tasks in deep neural networks reduce overall performance.
method Proposes a gradient regularization term to minimize task interference.
result Models with orthogonal gradients perform better on various datasets.
Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Co…
Deep network learns Obstacle Tower challenge without human demonstrations.
problem Master procedurally generated levels that get progressively harder.
method Deep Reinforcement Learning with a simple feed-forward network.
result Performed competitively in a reinforcement learning competition.
LLMs can collude in market divisions, maximizing profits.
problem Strategic collusion of LLM agents in multi-commodity markets.
method Examined LLMs in Cournot competition frameworks, analyzing pricing and resource allocation strategies.
result LLMs can monopolize specific commodities without direct human input or explicit collusion commands.
The paper proposes using experts' insights in machine learning tasks.
problem Lack of reliance on experts' opinions in machine learning tasks.
method A three-step framework for integrating experts' insights into machine learning models.
result Proposed models achieve more stable and lower log loss results compared to top solutions.
MAT uses attention mechanism for molecule property prediction.
problem Designing a competitive neural network for molecule property prediction.
method Augmented attention mechanism using inter-atomic distances and molecular graph structure.
result MAT achieves state-of-the-art performance on diverse molecular prediction tasks.
This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platform as "Freesound General-Purpose Audio Tagging Challenge". The goal of the task is to build an audio tagging system that can recognize the c…
Competition aims to develop sample-efficient RL methods using human demonstrations.
problem Exponential increase in samples needed for reinforcement learning.
method Develop sample-efficient algorithms using human demonstrations.
result Introduction of MineRL ObtainDiamond task and MineRL-v0 dataset.
The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data. If labels indices are considered as time indices, the problems can often be seen as equivalent. In this pape…
Advocacy learning uses competitive networks to improve classification accuracy.
problem Improving classification accuracy in attention-based classification problems.
method Two networks: Advocates and a Judge. Advocates produce class-conditional attention maps to convince the Judge of their class.
result Advocacy learning can lead to small improvements in classification accuracy over a baseline.
Efficient method for uncertainty estimation in DNNs with improved accuracy.
problem Vital assessment of deep neural networks' reliability in safety-critical applications.
method Multi-loss sub-ensembles for parallel predictions from similar models differing by their loss.
result Improved accuracy on classification tasks and competitive uncertainty measures.
Competition aims to develop sample-efficient reinforcement learning methods.
problem Limited access to samples in reinforcement learning.
method Develops algorithms leveraging human demonstrations in Minecraft.
result Fosters innovation in sample-efficient reinforcement learning.
Adaptive volatility method improves probabilistic financial forecasting.
problem Probabilistic forecasting in financial markets.
method Adapts classical time-varying volatility models with online stochastic optimization.
result Ranked 5th in M6 financial forecasting competition.
Meta-learning reformulated as Bayesian risk minimization.
problem Learning models to quickly adapt to new tasks from small datasets.
method Formalized meta-learning as Bayesian risk minimization, using a probabilistic framework to compute predictive distributions from posterior distributions of latent variables conditioned on contextual datasets.
result A novel Gaussian approximation for the posterior distribution that converges to maximum likelihood estimates and outperforms Neural Process on benchmark datasets.
Although a great methodological effort has been invested in proposing competitive solutions to the class-imbalance problem, little effort has been made in pursuing a theoretical understanding of this matter. In order to shed some light on this topic, we perform, through a novel framework, an exhaustive analysis of the …
Adversarial training was recently shown to be competitive against supervised learning methods on computer vision tasks, however, studies have mainly been confined to generative tasks such as image synthesis. In this paper, we apply adversarial training techniques to the discriminative task of learning a steganographic …
Private cancer prediction model trained on federated genomic data.
problem Train a private cancer prediction model on federated genomic data.
method Differentially private federated learning (FL) for genomic cancer prediction.
result Ranked 3rd in a competition for private cancer prediction.
New method uses nearest neighbors quantile filter for probabilistic energy forecasting.
problem Creating accurate probabilistic energy forecasts using complex data mining techniques.
method Uses a new nearest neighbors quantile filter to create quantile regressions without a non-differentiable cost function.
result Demonstrates superior performance in Global Energy Forecasting Competition 2014.
Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional autoencoders tend to learn possibly trivial representations of text documents due to their confounding properties such as high-dimensionality…
A number of modern learning tasks involve estimation from heterogeneous information sources. This includes classification with labeled and unlabeled data as well as other problems with analogous structure such as competitive (game theoretic) problems. The associated estimation problems can be typically reduced to solvi…
Paper tackles AI driving competition challenges with mixed simulation and real-world data.
problem AI algorithms perform poorly in real-world environments compared to simulated ones and vice versa.
method Employed imitation learning on a mixed dataset to train algorithms equally well in all environments.
result Trained algorithms performed well in both simulated and real-world environments.
Study competition in OTC CDS market through CCP and interdealer choice models.
problem Analyze competition dynamics in OTC credit default swap market.
method Developed models for CCP choice and interdealer counterpart selection using semi-supervised learning and game theory.
result Introduced novel metrics and algorithms for understanding market dynamics.
Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In particular, by working in scattering space, we achieve competitive results both for supervised and unsupervised learning tasks, while making pr…
Detects out-of-domain cases with limited training data.
problem Detecting out-of-domain cases with insufficient in-domain training data.
method Proposes an OOD-resistant Prototypical Network.
result Outperforms state-of-the-art methods in zero-shot OOD detection.
This paper presents details of our winning solutions to the task IV of NIPS 2017 Competition Track entitled Classifying Clinically Actionable Genetic Mutations. The machine learning task aims to classify genetic mutations based on text evidence from clinical literature with promising performance. We develop a novel mul…
FDN improves probabilistic regressors' adaptability to distribution shifts.
problem Overconfidence in modern probabilistic regressors under distribution shift.
method FDN uses input-conditioned distributions over network weights, trained with a Monte Carlo beta-ELBO objective.
result FDN produces predictive mixtures whose dispersion adapts to the input, providing shift-aware uncertainty.
A simple fix helps saliency methods pass sanity checks.
problem Saliency methods fail simple sanity checks.
method Compute saliency maps for all labels, use competition to identify and remove less relevant pixels.
result CGI method is efficient, requires no additional training, and uses only the input and gradient.
Fundamental frequency (f0) estimation from polyphonic music includes the tasks of multiple-f0, melody, vocal, and bass line estimation. Historically these problems have been approached separately, and only recently, using learning-based approaches. We present a multitask deep learning architecture that jointly estimate…
m-arcsinh improves SVM and MLP reliability and speed in scikit-learn.
problem Improving SVM and MLP reliability and speed in scikit-learn.
method Modified arcsinh function for kernel and activation in SVM and MLP.
result Competitive classification performance and reliability of SVM and MLP with m-arcsinh.
New method ranks competitors from multiple types of comparisons.
problem Ranking individuals or teams from multiple conflicting comparison types.
method Combination of expectation-maximization algorithm and modified Bradley-Terry model.
result A ranking can be computed from multiple conflicting comparison types.
User smeznar achieved 8th place in PGDL by predicting generalization of deep learning models.
problem Understanding and predicting generalization in deep learning models.
method Creating simple metrics and finding their best combination for automatic testing on a dataset.
result Combination of various properties of neural network architectures can be used for generalization prediction.
Proposes a multi-task learning model using variational information bottleneck.
problem Balancing performance and robustness across different tasks in multi-task learning.
method Variational Information Bottleneck (VIB) architecture for multi-task learning.
result The proposed model achieves competitive prediction accuracy under adversarial attacks.
Complex numbers have long been favoured for digital signal processing, yet complex representations rarely appear in deep learning architectures. RNNs, widely used to process time series and sequence information, could greatly benefit from complex representations. We present a novel complex gated recurrent cell, which i…
Combining causality, control, and reinforcement learning for system control.
problem Learning to control dynamical systems using causal, control, and reinforcement learning approaches.
method Combining causal identification, control strategies, and reinforcement learning to control dynamical systems.
result Combining different learning paradigms for effective system control.