Introduces the BAH dataset with 1,427 annotated videos for multimodal recognition of ambivalence/hesitancy in digital behavior change contexts.
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An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
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For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at http://github.com/locuslab/TCN .
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- abstract For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple conv
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representative citing papers
S4 is an efficient state space sequence model that captures long-range dependencies via structured parameterization of the SSM, achieving state-of-the-art results on the Long Range Arena and other benchmarks while being faster than Transformers for generation.
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Spacetime SSM forecasters represent optimal Kalman predictors for autoregressive data but remain vulnerable to model-free attacks that exploit local linearity and increase error by over 33% compared to projected gradient descent.
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Sundial uses TimeFlow Loss for native pre-training of Transformers on continuous time series from TimeBench, achieving SOTA point and probabilistic forecasting with millisecond inference.
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citing papers explorer
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BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change
Introduces the BAH dataset with 1,427 annotated videos for multimodal recognition of ambivalence/hesitancy in digital behavior change contexts.
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Efficiently Modeling Long Sequences with Structured State Spaces
S4 is an efficient state space sequence model that captures long-range dependencies via structured parameterization of the SSM, achieving state-of-the-art results on the Long Range Arena and other benchmarks while being faster than Transformers for generation.
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Scale-Equivariant Generative Forecasting: Weight-Tied Dilated Convolutions, Wavelet Scattering Inputs, and Spectral-Consistency Training for Self-Similar Time Series
Presents SE-WaveNet with weight-tied dilated convolutions plus wavelet and spectral components that reproduces empirical scaling collapse on financial returns while using L times fewer convolutional parameters.
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U-STS-LLM A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputation
U-STS-LLM uses a spatio-temporally steered LLM with dynamic attention bias generation to achieve state-of-the-art results on long-horizon traffic forecasting and high-missing-rate imputation while remaining parameter-efficient.
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TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
TailedTS supplies 24.69 billion Wikipedia page-view records as a public benchmark for heavy-tailed time series forecasting and periodicity analysis, revealing weaker periodic structure in high-traffic pages.
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SIGMA-ASL: Sensor-Integrated Multimodal Dataset for Sign Language Recognition
SIGMA-ASL is a multimodal dataset with 93,545 word-level ASL clips from Kinect RGB-D, mmWave radar, and dual IMUs, plus benchmarking protocols for single- and multi-modal recognition.
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AegisTS: A Hierarchical Agent System with Reinforcement Learning for Multivariate Time Series Data Cleaning
AegisTS uses a two-level RL agent architecture with a dual-stage reward to jointly optimize cleaning order and method selection for multivariate time series, delivering up to 96% better cleaning quality and 27% better downstream performance without ground truth.
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BadmintonGRF: A Multimodal Dataset and Benchmark for Markerless Ground Reaction Force Estimation in Badminton
BadmintonGRF is a new public multimodal dataset and benchmark that pairs multi-view video with instrumented GRF for markerless load estimation in badminton.
-
GAFSV-Net: A Vision Framework for Online Signature Verification
GAFSV-Net encodes online signatures as asymmetric Gramian Angular Field images and processes them with dual-branch ConvNeXt plus cross-attention to outperform sequence-based baselines on DeepSignDB and BiosecurID.
-
Autocorrelation Reintroduces Spectral Bias in KANs for Time Series Forecasting
Temporal autocorrelation reintroduces spectral bias in KANs for time series forecasting, which DCT preprocessing can mitigate.
-
A Convolutional Neural Network-Derived Catalog of Solar Flares from Soft X-Ray Observations
The CNN-derived catalog detects over seven times more solar flares than the GOES catalog and extends the power-law distribution of flare peak fluxes to smaller sizes.
-
Adversarial Robustness of Deep State Space Models for Forecasting
Spacetime SSM forecasters represent optimal Kalman predictors for autoregressive data but remain vulnerable to model-free attacks that exploit local linearity and increase error by over 33% compared to projected gradient descent.
-
Self-Supervised Foundation Model for Calcium-imaging Population Dynamics
CalM uses a discrete tokenizer and dual-axis autoregressive transformer pretrained self-supervised on calcium traces to outperform specialized baselines on population dynamics forecasting and adapt to superior behavior decoding.
-
T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
T1 uses one-to-one channel-head binding in a CNN-Transformer hybrid to achieve robust multivariate time-series imputation, cutting average MSE by 46% versus the next-best baseline across 11 datasets even at 70% missingness.
-
MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection
MELT is the first behavioral trace dataset for high-risk memecoin launch detection on Solana, providing 122 features, risk annotations, and ML benchmarks that reduce investment loss when used for selection.
-
Causal Time Series Generation via Diffusion Models
CaTSG is a unified diffusion model for causal time series generation that handles observational, interventional, and counterfactual tasks via backdoor adjustment and abduction-action-prediction.
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Sundial: A Family of Highly Capable Time Series Foundation Models
Sundial uses TimeFlow Loss for native pre-training of Transformers on continuous time series from TimeBench, achieving SOTA point and probabilistic forecasting with millisecond inference.
-
What Causes Performance Degradation in Cross-Subject EEG Classification?
Controlled experiments attribute cross-subject EEG classification degradation to inter-subject variability in multi-class tasks and shortcut learning in single-class tasks.
-
Deep Time Series Models: A Comprehensive Survey and Benchmark
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
-
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Time-LLM reprograms frozen LLMs for time series forecasting via text prototypes and Prompt-as-Prefix, outperforming specialized models in standard, few-shot, and zero-shot settings.
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Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
First benchmarking of ordinal adaptations of CNN and DL methods for time series shows they outperform nominal TSC techniques on ordinal metrics across 29 selected problems.
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
PatchTST uses subseries patching and channel-independent Transformers to deliver significantly better long-term multivariate time series forecasting and strong self-supervised transfer performance.
-
Tropical time series, iterated-sums signatures and quasisymmetric functions
Defines iterated-sums signatures over commutative semirings (tropical case emphasized) for time-series feature extraction and links them to quasisymmetric functions over semirings.
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ReactiveGWM: Steering NPC in Reactive Game World Models
ReactiveGWM introduces a decoupled diffusion architecture for player-NPC interactions that learns game-agnostic response logic for zero-shot strategy transfer across games.
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Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
A five-phase co-training framework enables stable JEPA pretraining on EHR trajectories, producing converging latent rollouts and higher multi-task AUROC than baselines on MIMIC-IV ICU data.
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What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.
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Dynamics Aware Quadrupedal Locomotion via Intrinsic Dynamics Head
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WISE-FM:Operation-Aware, Engineering-Informed Foundation Model for Multi-Task Well Design
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Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment
CCSS-RS achieves RMSE 0.696 and CRPS 0.349 at 1000-step horizons on a large public WWTP benchmark with 43% missingness, outperforming Neural CDE baselines by 40-46% in RMSE.
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Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Conditional attribution retrieves contextually similar normal states from VAE latent spaces and UMAP embeddings to explain time-series anomalies while preserving dependencies, improving root-cause accuracy on SWaT and MSDS benchmarks.
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VQ-Wave: A physics-driven spatio-temporal deep learning approach for non-contrast-enhanced lung ventilation and perfusion MRI
VQ-Wave is a physics-driven spatio-temporal neural network that learns to extract ventilation and perfusion maps from non-contrast lung MRI by training on simulated signals with amplitude modulations, frequency drifts, and noise, showing better stability than matrix pencil decomposition in phantoms,
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Modern Structure-Aware Simplicial Spatiotemporal Neural Network
ModernSASST is the first simplicial complex-based spatiotemporal model that combines random walks on high-dimensional complexes with parallelizable temporal convolutional networks for efficient high-order topology capture.
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AIBuildAI: An AI Agent for Automatically Building AI Models
AIBuildAI uses a manager agent and three LLM sub-agents to fully automate AI model development and achieves a 63.1% medal rate on MLE-Bench, matching experienced human engineers.
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MISID: A Multimodal Multi-turn Dataset for Complex Intent Recognition in Strategic Deception Games
MISID is a multimodal multi-turn dataset for intent recognition in strategic deception games, paired with the FRACTAM framework that improves MLLM performance on hidden intent detection via decouple-anchor-reason steps.
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BiTA: Bidirectional Gated Recurrent Unit-Transformer Aggregator in a Temporal Graph Network Framework for Alert Prediction in Computer Networks
BiTA redesigns temporal aggregation in TGNs by jointly using bidirectional GRU for sequential dependencies and Transformer for long-range context to improve alert prediction accuracy on real network data.
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A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography
TS2TC combines cross-temporal fusion generative anchor pretraining with dual-process transfer to achieve 2.49% lower RMSE than prior methods on PPG parameter estimation using only 10% labeled data.
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ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models
ROMAN converts time series into a shorter multiscale channel representation that lets standard CNN classifiers access scale and coarse-position information explicitly.
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WiFlow: A Lightweight WiFi-based Continuous Human Pose Estimation Network with Spatio-Temporal Feature Decoupling
WiFlow achieves 97.25% PCK@20 and 99.48% PCK@50 on continuous pose estimation from WiFi CSI using a 2.23M-parameter network trained on 360,000 synchronized samples from 5 subjects.
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Concurrence: A dependence criterion for time series, applied to biological data
Concurrence detects dependence between time series by training a classifier to separate aligned from misaligned segments.
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X-IONet: Cross-Platform Inertial Odometry Network for Pedestrian and Legged Robot
X-IONet combines rule-based platform classification with a dual-stage attention network to predict displacement and uncertainty from IMU data, then fuses outputs via EKF, achieving reported error reductions on pedestrian and quadruped datasets.
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Vision-LLMs for Spatiotemporal Traffic Forecasting
ST-Vision-LLM reframes spatiotemporal traffic forecasting as vision-language fusion, using visual encoders on traffic grids and efficient numerical tokenization to achieve 15.6% better long-term accuracy and 30% gains in few-shot cross-domain settings.
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Chinese Cyberbullying Detection: Dataset, Method, and Validation
Introduces CHNCI, the first Chinese cyberbullying incident detection dataset with 220,676 comments across 91 incidents, created via ensemble pseudo-labeling from explanation-generating methods followed by human annotation.
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Physically Interpretable World Models via Weakly Supervised Representation Learning
PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
By applying attention and feed-forward networks to inverted variate tokens instead of temporal tokens, iTransformer achieves state-of-the-art performance on real-world time series forecasting datasets.
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Simplified State Space Layers for Sequence Modeling
S5 uses a single MIMO state space model with S4-derived initialization to match S4 efficiency and reach 87.4% average accuracy on the Long Range Arena benchmark.
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Multi-task Self-Supervised Learning for Human Activity Detection
A multi-task self-supervised approach trains a temporal CNN to detect transformations on sensory data, yielding features that match or exceed fully supervised performance in semi-supervised and transfer settings for smartphone-based HAR.
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R-Transformer: Recurrent Neural Network Enhanced Transformer
R-Transformer integrates RNNs with multi-head attention to model local and global sequence dependencies without position embeddings and reports large-margin gains over prior methods on diverse tasks.
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Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
Sequential VAE embeds simulated trajectories into latent paths for Bayesian optimization with dynamic compression to enable data-efficient high-dimensional controller tuning on robots.
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UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.
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Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management
PBKV predicts agent invocations in dynamic LLM workflows to manage KV-cache reuse, delivering up to 1.85x speedup over LRU and 1.26x over KVFlow.