Neural User Simulator outperforms traditional ABUS in training dialogue systems.
problem Limited diversity and lack of natural language in ABUS.
method NUS learns user behavior from a corpus and generates natural language.
result NUS trained policies outperform ABUS in real user evaluations.
We introduce SIM-CE, an advanced, user-friendly modeling and simulation environment in Simulink for performing multi-scale behavioral analysis of the nervous system of Caenorhabditis elegans (C. elegans). SIM-CE contains an implementation of the mathematical models of C. elegans's neurons and synapses, in Simulink, whi…
Improves NMT with user feedback from eBay ratings and search tasks.
problem Improving neural machine translation quality with user feedback.
method Offline bandit learning of NMT parameters using real user feedback from eBay.
result Implicit task-based feedback from cross-lingual search tasks improves NMT quality.
Smartfluidnet accelerates Eulerian fluid simulation with neural networks.
problem Current neural network methods for Eulerian fluid simulation lack flexibility and generalization.
method Smartfluidnet automates model generation and dynamic switching to meet user requirements.
result Smartfluidnet achieves 1.46x and 590x speedup compared to state-of-the-art models, with better simulation quality.
Python package for fast simulation-based inference.
problem Intractable likelihood functions in Bayesian inference.
method Uses neural networks as surrogate models for Bayesian inference.
result Highly efficient and user-friendly for constructing SBI estimators.
The paper uses neural networks to predict customer conversions and retarget ads.
problem Predicting customer conversions and optimizing advertising strategies.
method Modelled customer online behaviors using RNN and CNN, estimating conversion rates with Monte Carlo simulation.
result Demonstrated the feasibility of using neural networks for customer behavior prediction and advertising.
A neural network method optimizes power control in multi-user channels.
problem Maximizing sum rate in multi-user interference channels.
method PCNet, a multi-layer neural network, directly maximizes sum rate; ePCNet is an ensemble of multiple PCNets.
result ePCNet outperforms existing power control methods by 1.2%-4.6%.
Framework synthesizes programs for simulating complex models and estimating parameters.
problem Parameter estimation for complex models requires manual encoding of fixed model structures.
method Combines LLMs for program synthesis with neural simulation-based inference.
result Identifies plausible model families from open-ended prompts with high accuracy.
New method detects and measures malicious users in recommendation algorithms.
problem Identifying and quantifying malicious user activity in recommendation systems.
method Probabilistic programming for a disentangled model of malicious and regular user behavior.
result Simulation-based measure for quantifying malicious user effects.
RecSim creates customizable simulation environments for RSs.
problem Creating realistic simulation environments for RSs.
method Configurable platform for authoring simulation environments.
result Enables pushing the limits of RL and RS techniques.
DPConvCNP learns to predict private data accurately and privately.
problem Balancing privacy and accuracy in machine learning models.
method Meta-learning combined with improved DP mechanism.
result DPConvCNP outperforms DP GP baseline, especially on non-Gaussian data.
CHAMELEON uses RNNs to recommend news sequences better than other methods.
problem Improving news recommendation accuracy and catalog coverage.
method Hybrid meta-architecture CHAMELEON with RNNs for sequence modeling and side information.
result Significantly higher recommendation accuracy and catalog coverage.
APES simplifies reinforcement learning environment design in Python.
problem Creating and simulating reinforcement learning environments.
method Introduces APES, a Python toolbox for 2D grid-world environments.
result Equips reinforcement learning agents with customizable field of vision and item/reward interactions.
BayesFlow trains neural networks for fast Bayesian inference.
problem Fast Bayesian inference for complex models.
method Amortized neural networks for intractable posterior distributions.
result Fast inference through pre-trained neural networks.
Paper uses RNN to predict SaaS user lifetime value.
problem Predicting user lifetime value in SaaS applications.
method Recurrent Neural Network with multi-cell architecture, accounting for cohort, age-in-system, and contemporaneous information.
result Significantly improved prediction accuracy compared to existing models.
New ABC method uses neural emulators for inference in complex models.
problem Approximate Bayesian Computation (ABC) for models without tractable likelihoods.
method Probabilistic neural emulator networks to learn synthetic likelihoods, adaptive simulations.
result Accurate and efficient inference on high-dimensional problems.
Proposes a multi-perspective neural model for better user preference analysis in recommendation systems.
problem Complex user preferences not well captured by existing recommendation models.
method Uses multiple perspectives to encode user and item features, then combines these representations to predict ratings.
result Significant improvements over baseline models in predicting user ratings.
Algorithm learns user's reward function from hypothetical behaviors.
problem Aligning agent behavior with unknown user objectives.
method Synthesizes hypothetical behaviors, asks user for rewards, trains neural network.
result Significantly outperforms prior methods in learning reward models.
The paper examines how different fusion strategies in neural networks affect user embeddings and their quality.
problem The need for automated processing of user data, particularly in predicting ratings and estimating user similarity.
method Analyzed the effect of various fusion strategies in neural networks on user embeddings quality and prediction performance.
result Fusion strategies in neural networks affect both embedding quality and prediction performance, and prediction performance does not necessarily reflect embedding quality.
Proposes CF-SFL to improve sparse data recommendation.
problem Poor performance of CF in sparse data.
method Generative user feedback loop to simulate user feedback.
result Improves recommendation results on multiple datasets.
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluat…
Proposes D2D-LSTM for predicting mobile social network content diffusion paths.
problem Lack of accurate content popularity prediction considering time and location in mobile social networks.
method D2D-LSTM, a deep neural network combining user social features and files features.
result Significantly improved prediction accuracy (up to 85.858%) and faster convergence (less than 100 steps).
PDSim simulates and estimates commodity futures prices using polynomial diffusion models.
problem Simulating and estimating commodity futures prices using polynomial diffusion models.
method Developed an R package with a Shiny app for simulation and estimation of commodity futures prices using polynomial diffusion models.
result PDSim is the only package specifically designed for the simulation and estimation of the polynomial diffusion model.
Improved neural model for social recommendation by integrating social and interest networks.
problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.
New method for state inference in state-space models with unknown dynamics.
problem State inference in state-space models with computationally expensive and undefined dynamics.
method Estimate state transition dynamics using a multi-output Gaussian process and Bayesian Neural Network as a surrogate model.
result Significant improvement in accuracy for state inference and prediction in non-stationary user models.
Proposes a graph neural network for personalized news recommendation.
problem Data sparsity in news recommendation systems.
method Heterogeneous graph model + Graph Neural Networks + LSTM attention mechanism.
result Significantly outperforms state-of-the-art methods on news recommendation datasets.
Study compares deep learning and superlearner architectures for classification problems.
problem Assessing performance and computational efficiency of different machine-learning methods.
method Comparative analysis of superlearner and deep learning architectures across various datasets.
result Superlearner outperforms deep neural networks on nonlinear predictor relationship datasets, suggesting faster convergence and interpretability.
ARNN augments RNNs with user-contextual preference for better session-based recommendations.
problem Limited context-awareness in RNN session models.
method Proposes ARNN that uses PNN to extract high-order user-contextual preference.
result ARNN outperforms baseline RNN by a large margin with rich user-side contexts.
Paper presents a reinforcement learning framework for personalized music playlist generation.
problem Misalignment between offline model objectives and online user satisfaction metrics in conventional playlist recommendation methods.
method Simulation-based reinforcement learning approach using a Deep Q-Network (DQN) modified to address large state and action spaces.
result The modified DQN (AH-DQN) policy leads to better user-satisfaction metrics compared to baseline methods during online A/B tests.
AI2V learns user representations by focusing on recent interests.
problem User interests and behavior change over time, affecting recommendation quality.
method Introduces AI2V, a neural attentive model that learns user representations by focusing on recent interests.
result AI2V outperforms other models on various datasets.
A hybrid approach uses RNNs to recommend news articles based on context and session history.
problem Challenging news recommendation due to varying user interests and factors.
method Context-aware, hybrid, deep learning approach using RNNs with additional information types.
result Significantly higher recommendation accuracy and catalog coverage compared to other session-based algorithms.
TensorQuant simulates quantization of DNNs for efficient computation.
problem Optimal quantization method and bit number for DNNs are topology-dependent.
method TensorQuant simulates quantization of existing DNN topologies.
result TensorQuant supports experimental evaluation of quantization impact.
Neural pedagogical agent updates user models in real-time for mobile education apps.
problem Real-time user modeling for dynamic mobile education platforms.
method Bidirectional recurrent neural networks with attention mechanism over embedded question-response pairs.
result Model outperforms existing approaches in predicting user response correctness.
Deep neural networks improve recommendation accuracy in marketplaces.
problem Measuring and optimizing recommender performance in marketplaces.
method Hybrid item representation models, sequence-based models, and multi-armed bandit models.
result Promising deep neural network recommenders are currently in production at FINN.no.
Graph-based MAB system improves recommendation accuracy.
problem Improving recommendation accuracy in user space.
method Proposes a graph-based recommendation system that learns user space geometry.
result Simulation results show improvements over state-of-the-art MAB algorithms.
Deep learning identifies smartphone users from motion sensor data.
problem Smartphone user identification using motion sensor signals.
method Transformed motion signals into images, trained CNN for classification, used SVM for few-shot identification.
result CNN achieved 89.75% multi-class user classification and 96.72% few-shot user identification accuracy.
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.
FURL improves model accuracy in FL by locally training user embeddings.
problem Improving prediction accuracy of neural-network-based models in Federated Learning.
method FURL divides model parameters into federated and private parameters, training private parameters locally.
result Significant performance improvement with 8% and 51% increases on two datasets.
RNNs excel at both short-term and long-term user interaction prediction.
problem Modeling user preferences over short and long time horizons.
method Evaluating RNN-based models on both short-term and long-term recommendation tasks.
result RNNs can predict immediate and distant user interactions.
A novel RNN survival model predicts web user return times.
problem Predicting when web users will return.
method Developed a novel RNN survival model that combines RNN's feature learning with survival analysis's non-returning user representation.
result Successfully predicts return times with superior discrimination between returning and non-returning users.
This paper tackles efficient resource control in IoT edge computing using deep reinforcement learning.
problem Efficient allocation and scheduling of limited resources in IoT edge computing systems.
method Formulated as a CTMDP model, used deep reinforcement learning (RL) to approximate value functions and solve the MDP problem.
result Significant performance improvement over baseline algorithms and RL algorithms based on other architectures.
The paper models user-advertiser interactions using point processes.
problem Causal inference problems in user-advertiser interaction.
method Temporal marked point processes and neural point processes.
result Neural point processes as practical solutions.
Novel SNN achieves 99.80% accuracy on MNIST handwritten digits.
problem Real-time handwritten digit classification.
method Fixed synaptic weight maps for feature extraction and NormAD algorithm for weight adjustment.
result 7x fewer parameters than state-of-the-art networks, real-time predictions within 100ms.
Automates decision-making for human operators managing multiple robots.
problem Limited human operator attention when controlling multiple robots.
method Learned model of user preferences from easy settings to automatically identify the most critical robot.
result Automated decision-making can assist human operators in managing more robots than their attention allows.
Modeling dynamic user interests using neural matrix factorization.
problem Challenging extraction of valuable insights from unstructured, high-dimensional, and dynamic online content data.
method Combines matrix factorization with neural networks to model nonlinear user and content factors.
result Accurately identifies nuanced and coherent consumption patterns of Boston Globe readers over five years.
Algorithmic recommendation systems can homogenize user behavior, reducing utility.
problem Algorithmic feedback loops homogenize user behavior in recommendation systems.
method Simulations of recommendation systems using confounded data.
result Using confounded data decreases utility without increasing diversity.
NGMs create mirrored features to assess neural network feature importance.
problem Lack of feature relevance information in DNNs limits their applicability.
method Structured perturbation and kernel-based conditional dependence measure for feature importance evaluation.
result Controls feature selection error rate and maintains high selection power with correlated features.
Deep learning optimizes user association in Massive MIMO networks.
problem Optimizing user cell association for maximum sum-rate in Massive MIMO networks.
method Training a deep neural network to learn optimal association rules based on user positions.
result The neural network achieves the same performance as traditional optimization methods with reduced computational complexity.