A method predicts driving intentions of human-driven vehicles for safer autonomous driving.
problem Predicting timely driving intentions of human-driven vehicles for autonomous vehicles in mixed traffic.
method A Hidden Markov Model (HMM) approach using continuous mobility features.
result HMMs trained with continuous mobility features improve prediction accuracy.
Proposes a method to predict vehicle intentions and motion adaptively.
problem Accurately predicting vehicle behaviors in various traffic scenarios.
method Probabilistic framework based on deep neural network.
result Better long-term motion prediction performance.
PIP-Net predicts pedestrian crossing intentions with up to 4-second lead.
problem Accurate pedestrian intention prediction for autonomous vehicles in real-world scenarios.
method Recurrent and temporal attention-based model using kinematic and spatial features.
result PIP-Net predicts pedestrian crossing intentions up to 4 seconds in advance.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
Proposes CTSDG model for better vehicle intention prediction across domains.
problem Domain generalization for vehicle intention prediction in dynamic environments.
method Structural causal model with recurrent latent variable integration.
result Consistent improvement in prediction accuracy compared to state-of-the-art methods.
Study predicts adolescents' intention to smoke cigarettes using ML models.
problem Early identification of adolescents' smoking intentions.
method Five machine learning algorithms tested for predicting intention to smoke cigarettes.
result Gradient Boosting Classifier showed highest accuracy.
Predicts customer call intent for auto dealerships using CNN.
problem Understanding customer intent from phone calls for better service.
method Developed a CNN-based supervised learning model for multi-class classification.
result CNN model performs well on customer call intent classification.
DSPN predicts advertiser satisfaction and intent for e-commerce platforms.
problem Understanding advertiser intent and satisfaction for e-commerce platforms.
method Two-stage Deep Satisfaction Prediction Network (DSPN) that models intent and satisfaction.
result DSPN outperforms state-of-the-art baselines and predicts advertiser satisfaction accurately.
TPG-DNN predicts user intent using GRU loss and multi-task learning.
problem Improving user experience and shopping efficiency in e-commerce.
method Adaptive GRU loss function with multi-task learning.
result TPG-DNN outperforms existing CTR models on Taobao datasets.
Deep neural network detects driver intentions from video.
problem Detecting driver intentions for safer self-driving.
method Uses deep learning to analyze turn signals and emergency flashers.
result High per-frame accuracy in challenging scenarios.
Neural network predicts purchasing intent without feature engineering.
problem Predicting purchasing intent in ecommerce with minimal feature engineering.
method Trainable vector spaces, multi-layer recurrent neural networks, parameter sharing, skip connections.
result Classification accuracy exceeds state-of-the-art on benchmark datasets.
Robots learn intentions from multiple cues to reduce uncertainty.
problem Uncertainty in human-robot interaction for vulnerable users.
method Multimodal classifier fusion using Bayesian Independent Opinion Pool.
result Fused classifiers outperform individual modalities in accuracy and uncertainty reduction.
Study proposes a time-aware model to predict user conversion intent.
problem Weak predictive signals from users not suitable for conversion prediction.
method Time-aware approach to model user activities and capture conversion intent signals.
result Approach outperforms other models on real-world datasets.
New IRL framework divides behavior into subgoals for better prediction.
problem Handling changing intentions and diverse trajectories in reinforcement learning.
method Nonparametric spatio-temporal subgoal modeling for more efficient and compact behavior representation.
result Framework significantly outperforms existing IRL solutions and handles time-varying intentions.
Researchers analyze how RNNs solve intent detection tasks using dynamical systems theory.
problem Understanding the internal mechanisms of RNNs in intent detection.
method Investigating RNN architectures through a dynamical systems perspective.
result Identified fixed point topology and limited number of attractors in RNN dynamics.
New RL method learns from passive data by modeling intentions.
problem Learning from passive data like videos without rewards or actions.
method Model intentions using temporal difference learning, learning representations from raw data.
result Successfully learns features from passive data that accelerate downstream RL tasks.
SYNTHONY selects tabular synthesizers based on stress profiling and user intent.
problem Non-uniform performance of tabular generative models across datasets.
method Stress profiling and intent-conditioned tabular synthesis selection.
result Meta-features predict synthesizer performance, improving selection accuracy.
This paper compares machine learning methods for recognizing lane change intentions from vehicle trajectories.
problem Accurately detecting and predicting lane change processes in autonomous vehicles.
method Comparison of different machine learning methods on high-dimensional time series data.
result Ensemble methods reduce Type II and Type III classification errors, while LightGBM outperforms XGBoost in training efficiency.
This research predicts vehicle movements by analyzing their intentions relative to road lanes.
problem Accurately forecasting vehicles' future movements for safe autonomous driving.
method LSTM networks with attention mechanisms applied to spatio-temporal graphs of road lanes.
result The model outperforms other state-of-the-art models in several metrics.
Improved financial VA intent classification accuracy.
problem Determining user intents for unseen open intents.
method Supervised pre-training of intent representations using prefix-tuning and fine-tuning.
result 1.63% - 2.07% higher accuracy on banking77 benchmark.
TIM framework uses LLMs and domain experts to infer DeFi user transaction intents.
problem Challenges in understanding user intent in DeFi transactions due to complex interactions and opaque logs.
method TIM framework leverages a DeFi intent taxonomy, multi-agent LLM system, and a Meta-Level Planner.
result TIM significantly outperforms existing methods in inferring user transaction intents.
New method infers intent from suboptimal behavior by modeling incorrect internal beliefs about dynamics.
problem Inferring intent from suboptimal human behavior using traditional methods assumes near-optimality, which is not always valid.
method Model suboptimal behavior as internal model misspecification, estimating incorrect beliefs about dynamics.
result Accurately models human intent by accounting for internal model inaccuracies.
New model learns coupled representations for domains, intents, and slots.
problem Representation learning for domains, intents, and slots in spoken language understanding.
method Proposes a model that learns coupled representations by aggregating slot and intent representations based on their hierarchical relationships.
result Improved performance on contextual cross-domain reranking task.
Paper addresses bias in search intent affecting click behavior.
problem Bias in user search intent affects click behavior and relevance.
method Proposes a search intent bias hypothesis to improve click models.
result Click models can better interpret user clicks and improve retrieval performance.
New bounds on predicting agent behavior from behavior alone.
problem Predicting agent beliefs and intentions from observed behavior.
method Derivation of bounds on agent behavior in new environments under assumption of world model.
result Theoretical limits on predicting intentional agents from behavioral data.
OCC system speeds up in-app communications for Uber drivers and riders.
problem Improving efficiency in in-app messaging between drivers and riders.
method Intents are detected using unsupervised embedding and nearest-neighbor classifier. Replies are retrieved based on popularity in historical data.
result System achieves 76% accuracy in intent detection and 71% adoption rate of smart replies.
Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This approach translates the multilingual search problem into a particular task of searc…
Paper predicts future vehicle trajectories for safer AVs.
problem Improving accuracy of long-term vehicle trajectory prediction for autonomous vehicles.
method Dual LSTM network for automatic learning of driver behaviors and future trajectory prediction.
result The method achieves lower RMSE for longitudinal and lateral predictions compared to state-of-the-art methods.
GWINs improve classifier accuracy by translating uncertain observations.
problem Improving accuracy of uncertain observations in classifiers.
method Generative network recovers correct observation distributions, reject option allows for uncertain predictions.
result GWINs significantly improve classifier accuracy on benchmark datasets.
Improved pedestrian crossing prediction for AVs using contextual factors.
problem Accurate prediction of pedestrian crossing behavior for autonomous vehicles.
method Factored Latent-Dynamic Conditional Random Fields (FLDCRF) for multi-label sequence prediction and joint interaction modeling.
result Achieved at least 0.9 seconds earlier prediction accuracy for pedestrian crossing behavior compared to existing methods.
Expanding spoken language understanding to handle complex entities and intents.
problem Handling compound entities and intents in spoken language understanding.
method Introducing a domain-agnostic shallow parser that handles linguistic coordination, learning domain-independent and slot-independent features.
result The model learns to segment conjunct boundaries of various phrasal categories and improves generalization across different slot types using adversarial training.
ACI converts call center conversations into actionable data.
problem Real-time spoken language understanding for call center conversations.
method Combines speech recognition, entity and intent recognition, and a business rules engine.
result ACI converts live audio into structured events for real-time supervision and assistance.
We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a su…
LSTM network aids intent classification in QA.
problem Classifying intent in question-answering.
method Used LSTM architecture for intent classification.
result Effective and efficient intent classification achieved.
AdvMind detects adversary intent in black-box attacks with high accuracy.
problem Detecting adversary intent in black-box adversarial attacks is challenging.
method AdvMind accounts for adversary adaptiveness and synthesizes queries to expose intent.
result AdvMind detects adversary intent with over 75% accuracy after observing less than 3 query batches.
Intelligent recommender system tracks user activity and intent for better recommendations.
problem Recommender systems often lack user intent awareness, leading to suboptimal recommendations.
method Encoded user activity, reduced to lower dimensions using tensor factorization, and scored for intent. Combined with contextual information for ranking recommendations.
result Better recommendations compared to baselines, with intent-aware scoring.
Adapts large transformer model for search query intent understanding.
problem Understanding and predicting user intents from search queries.
method Adapts BERT-like architecture for search queries, accounts for query noisiness and sparseness.
result Builds a shareable deep learning model for query intent identification.
Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research. Traditional approaches either rely on hand-crafting a small state-action set for applying reinforcement learning that is not scalable or constructi…
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recomme…
Proposes a new method for intent classification in student and enrollee questions.
problem Challenges of intent classification in specific subject areas with limited training data.
method Uses Shannon entropy to generate low-dimensional question vectors.
result Outperforms existing methods in intent classification task with small datasets.
This paper examines feature selection for extracting user intentions from Twitter.
problem Extracting user intentions from informal, misspelled tweets.
method Developed a dataset from Twitter feeds, used two feature selection techniques (Information Gain and hybrid forward selection), and applied four classification algorithms.
result The hybrid feature selection approach outperformed the Information Gain method.
Model improves email-based conversational agents' ability to extract relevant information.
problem Asynchronous email communication makes it hard for agents to detect intents and extract relevant entities.
method Neural model for scoping relevant information from large queries.
result Improves performance of intent detection and entity extraction tasks by 35% in precision.
Study reveals opacity in insider sales, leading to inefficiencies in capital allocation.
problem Insider sales opacity due to reporting inversion of Form 144 and Form 4.
method Event study framework, machine learning audit, cross-sectional tests.
result Persistent opacity of insider sales signals, leading to inefficiencies in capital allocation.
Defines devices and agents based on behavior, using computational theory.
problem Differentiating between systems described by mechanical and intentional stances.
method Formal definition of devices and agents, using Bayes' rule to calculate subjective probability based on behavior.
result Bayesian approach to distinguishing between mechanical and intentional systems.
Unified framework for deep learning with crowdsourced data.
problem Learning true labels from noisy, sparse, and uncontrolled crowdsourced annotations.
method Bayesian deep learning framework that learns annotator expertise and optimizes model training.
result Framework reduces annotation and training time for deep learning models.
TableQnA answers web queries about lists and superlatives from HTML tables.
problem Answer web queries about lists and superlatives from HTML tables.
method Extract intent from queries, use structure-aware matching, and train models with automatic data generation.
result Significantly higher precision and coverage for list and superlative queries.
Seq-CVAE learns a latent space for each word position to capture sentence intention.
problem Capturing diversity in image captioning models.
method Seq-CVAE learns a sequential latent space for each word position, mimicking future sentence summaries.
result Significantly improves diversity metrics on MSCOCO dataset compared to baselines.
Paper improves Native ads CTR prediction using event embeddings and recurrent networks.
problem Hard CTR prediction for Native ads due to lack of direct query intent.
method Proposes a large-scale event embedding scheme and a recurrent neural network model.
result Significantly outperforms baseline and variants in CTR prediction.