Survey reviews machine learning for automatic movement generation.
problem Need for automatic movement animation in interactive media.
method Machine learning techniques and motion capture data.
result Discussion of research gaps and challenges for future work.
Paper reduces movement primitive dimensionality in parameter space.
problem High dimensionality of movement primitives makes policy optimization expensive.
method Investigates dimensionality reduction in parameter space, identifying principal movements.
result Dimensionality reduction in parameter space is more effective than in configuration space.
Wearable smart suit tracks infant movements with high accuracy.
problem Early detection of atypical motor development in infants.
method Developed a multi-sensor smart suit for data collection, trained a deep CNN algorithm for automatic posture and movement classification.
result Setup achieves human equivalent accuracy in infant posture and movement classification.
Paper proposes a method for learning and inferring movement using deep generative models.
problem Challenges in learning and inferring movement due to high dimensionality and varied environments.
method Formulates motion planning as learning on a directed graphic model and uses deep generative models.
result Flexibly incorporates task descriptors and context information for long-term planning.
Deep convolutional architecture identifies eye movements for biometric faster and more accurately.
problem Biometric identification of eye movements for authentication.
method Developed a deep convolutional architecture to process raw eye-tracking signals.
result Achieved a lower error rate by one order of magnitude and faster identification time by two orders of magnitude.
Bayesian method infers local rules for collective animal movement.
problem Learn local rules governing long-term group behaviors.
method Bayesian Inverse Reinforcement Learning with Linearly-Solvable Markov Decision Process.
result Recover true costs and find value of collective movement.
Neural nets predict user attention from mouse movements.
problem Predicting user attention from mouse cursor movements.
method Investigated different mouse movement representations and trained neural networks.
result Neural networks outperform handcrafted features for predicting user attention.
Generically learns movement control policies from exploration data.
problem Movement optimization in physically based characters.
method Parameterizes actions as target states, learns low-level control policy.
result Improves movement optimization across multiple tasks and algorithms.
DeepMove learns place representations from large movement data.
problem Lack of movement context in traditional place representations.
method Directly models movements between places using deep learning.
result DeepMove outperforms state-of-the-art methods in place categorization and clustering.
Study identifies key trades predicting market movements.
problem Predicting future market price movements.
method Optimized neural network predictor to identify influential trades.
result Trades with specific characteristics significantly impact future price predictions.
Robotics learns new skills faster by reusing past movements.
problem Learning new motor skills is time-consuming and requires exploration of a large space of motor configurations.
method Combines probabilistic movement primitives with relative entropy policy search for skill initialization and adaptation.
result Quality of learned skills improves and the number of required iterations to learn a new task can be reduced by more than 60%.
Deep learning predicts VR head movements for better 360-degree videos.
problem Predicting head movements in 360-degree VR videos.
method Deep learning network using position data and video content for long-term prediction.
result 16.1% improvement in prediction accuracy compared to baseline.
Paper adapts and learns movement primitives robustly with probabilistic models.
problem Learning and adapting movement primitives with variability and correlations.
method Prior distributions and general purpose operators for robust parameter estimation and adaptation.
result Robust learning and adaptation achieved with fewer training instances and task-specific heuristics.
Paper uses ML to predict stock price movements from order book data.
problem Forecasting stock price movements in financial markets.
method Combines handcrafted and ML features for three classifiers, evaluated on two setups.
result Machine Learning shows promise for this task, suggesting future research.
Paper proposes a deep learning approach for hand movement classification from EEG.
problem Classifying hand movements from EEG for brain-computer interfaces.
method Uses a deep attention-based LSTM network to analyze EEG signals.
result Improves classification accuracy over benchmarks and state-of-the-art methods.
Robots learn movement libraries by segmenting complex trajectories.
problem Segmenting complex robot movement demonstrations for library building.
method Model trajectories as Switching Linear Dynamical Systems and infer segmentation using a nonparametric Bayesian approach.
result Robots can learn movement libraries more effectively by segmenting demonstrations.
This paper predicts weekly stock market movements using machine learning and introduces a new benchmark.
problem Predicting stock market movements using daily data and various ML models.
method Focuses on weekly movements, introduces random traders as a benchmark, uses additional features, and adjusts training datasets.
result Trained models, especially MLP, show good performance across different trends.
Recognizes collective sheep movement activities online.
problem Recognizing collective animal movement activities.
method Discriminative framework for tracking flock positions and velocities online.
result Good accuracy in learning skewed collective activities.
Wrist movements can reveal digits, posing security risks.
problem Security vulnerabilities in wrist wearable devices.
method Machine learning model trained on wrist movement data.
result 100% accuracy in predicting digits via wrist movement.
A new method reduces data movement in neural network training.
problem Large data movement during neural network training.
method Streaming batch principal component analysis for low-rank updates.
result Effective training of convolutional neural networks with low overhead.
Study uses wearable sensors to predict infant motor development status.
problem Early detection of infant motor development status in high-risk infants.
method Machine learning classification algorithms using day-long inertial movement data from wearable sensors.
result Machine learning can predict infant motor development status from day-long movement data.
Deep learning detects stereotypical motor movements in autism.
problem Detecting stereotypical motor movements in autism with high accuracy.
method Employing deep learning to learn features from multi-sensor accelerometer signals.
result Deep learning achieves higher accurate SMM detectors in longitudinal scenarios.
Optimizing data movement significantly improves transformer training efficiency.
problem Data movement is a major bottleneck in training transformers.
method Developed a recipe to globally optimize data movement in transformers.
result Achieved up to 1.30x performance improvement over state-of-the-art frameworks.
Deep learning detects protective movement behavior in chronic pain patients.
problem Detecting protective movement behavior in chronic pain patients for intervention.
method End-to-end deep learning architecture named BodyAttentionNet (BANet) that learns temporal and bodily parts.
result Statistically significant improvements in detecting protective behavior using attention mechanisms.
A deep learning framework assesses physical rehabilitation exercises.
problem Lack of versatile, robust, and practical assessment methods for rehabilitation exercises.
method Deep learning framework with metrics, scoring functions, and neural networks.
result First implementation of deep neural networks for rehabilitation performance assessment.
Automatic detection of reflective thinking in math problem solving using body movement data.
problem Detecting reflective thinking in children's mathematical problem solving.
method WeDraw-1 Movement Dataset, Long Short-Term Memory neural networks, end-to-end detection.
result Average F1 scores of 0.73 for automatic detection and 0.79 for end-to-end detection of reflective thinking.
Machine learning struggles to predict binary options movements due to randomness.
problem Predicting binary options movements using machine learning.
method Tested multiple machine learning models (RF, LR, GB, kNN) and neural networks (MLP, LSTM) on EUR/USD currency pairs.
result None of the models surpassed the ZeroR baseline accuracy, indicating randomness in binary options.
NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.
problem Sparse and nonstationary movement speed data from urban roads.
method Nonstationary Temporal Matrix Factorization (NoTMF) model.
result NoTMF outperforms baseline models in forecasting urban road movement speeds.
Deep learning predicts stock movements using social media data.
problem Predicting stock movements with traditional methods is challenging.
method Graph neural network combining financial data and social media.
result Improvement of 28% in cumulative returns.
Paper uses CNN to predict stock price movement as an image classification problem.
problem Predicting stock price movement using machine learning.
method CNN-based model for classifying stock price movement based on the first hour of trading.
result The algorithm effectively separated between stock price movement classes and outperformed other strategies.
Hybrid model predicts stock prices using ML, DL, and NLP.
problem Improving prediction accuracy of stock price movement.
method Machine learning, deep learning, natural language processing, sentiment analysis.
result LSTM model outperforms traditional machine learning models.
ChatGPT enhances GNN for stock movement prediction.
problem Predicting stock movements using textual data.
method Integrates ChatGPT's graph inference into GNN for stock movement forecasting.
result Model outperforms state-of-the-art benchmarks in stock movement forecasting.
We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to da…
We recast the Calabi flow in DeGiorgi's language of minimizing movements. We establish the long time existence of minimizing movements for K-energy with arbitrary initial condition. Furthermore we establish some a priori regularity of these solutions, and that sufficiently regular minimizing movements are smooth soluti…
LLT improves cryptocurrency price movement prediction accuracy.
problem Predicting intraday price movements of cryptocurrencies.
method Linear law-based feature space transformation (LLT) applied to cryptocurrency price data.
result LLT significantly enhances prediction accuracy for all cryptocurrencies.
Taureau uses Twitter sentiment analysis to predict stock market movement.
problem Predicting stock market movement using public opinion on Twitter.
method Obtained historical tweets, filtered and labeled, generated word embeddings, assessed sentiment scores, correlated with stock price movement, designed and evaluated predictive model.
result Taureau can predict stock price movement from lagged sentiment scores.
LARA forecasts financial asset trends by refining noisy labels and extracting profitable samples.
problem Low signal-to-noise ratio and stochastic nature of financial data lead to poor predictions.
method LARA combines LA-Attention and RA-Labeling to refine and extract profitable samples.
result LARA significantly outperforms existing methods on Qlib platform.
Deep learning detects sleep events like arousals and leg movements.
problem Detecting arousals and leg movements in polysomnogram for sleep disorders.
method Deep learning model trained on 1,485 subjects, tested on 1,000 recordings.
result Optimal detection achieved with dynamic event window for arousals and static window for leg movements.
CGANs forecast art movements by generating sequences of paintings.
problem Predicting the evolution of art movements over time.
method Trained CGANs on sequences of paintings, using VAR models for forecasting.
result CGANs accurately predict future art movements and generate plausible paintings.
Bayesian deep learning predicts price movements from LOBs, improving trading profits.
problem Predicting price movements from limit order books for better trading decisions.
method Applies dropout variational inference to deep neural networks, using uncertainty information for position sizing.
result Bayesian techniques improve predictive performance and deliver useful uncertainty information for trading.
Proposes deep mixture models for probabilistic price movement forecasting in high-frequency trading.
problem Probabilistic forecasting of price movements in high-frequency trading.
method Deep recurrent neural networks with probabilistic mixture models.
result Outperforms benchmark models in both metric-based and simulated trading scenarios.
Enhanced deep learning model predicts stock price movement using LOB data.
problem Challenges in predicting stock price movement from high-dimensional, volatile LOB data.
method Siamese architecture with multi-head attention and LSTM modules.
result Significant improvement in stock price prediction performance over strong baselines.
Model predicts stock prices using GAN and RoI Pooling.
problem Predicting stock prices influenced by macroeconomic factors.
method Markov Decision Process, GAN, RoI Pooling.
result Identifies macroeconomic factors' influence on stock prices.
The study models market price movement based on investors' expectations.
problem Understanding the dynamics of investors' expectations and market price movement.
method Developed a non-linear evolutionary equation linking investors' expectations and market asset price movement.
result Model predictions co-integrated with asset time series, suggesting potential for price movement forecasting.
ChatGPT struggles in predicting stock movements, underperforming traditional methods.
problem Predicting stock market movements using ChatGPT.
method Zero-shot analysis of ChatGPT's multimodal stock prediction capabilities.
result ChatGPT underperforms traditional methods and state-of-the-art models in predicting stock movements.
Paper proves minimizing movements match smooth droplet flow in 3D.
problem Consistency of minimizing movements with smooth mean curvature flow.
method Proved minimizing movements coincide with smooth droplet flow.
result Minimizing movements and smooth mean curvature flow are consistent in 3D.
Predict stock movement by considering cross effects among stocks.
problem Challenges in predicting stock price movement due to cross effects among stocks.
method Multi-GCGRU framework combining GCN and GRU, encoding cross effects from financial domain knowledge and data-driven relationships.
result Our model outperforms other baselines in predicting stock movement.
MPANF improves naive forecast by incorporating directional information.
problem Challenging to surpass naive forecast in financial time series.
method Combines naive forecast with movement prediction and accuracy.
result MPANF generally outperforms common benchmarks.