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Found 12 papers, 4 papers with code
Birdie: Advancing State Space Models with Reward-Driven Objectives and Curricula
In this paper, we propose a novel training procedure, Birdie, that significantly enhances the in-context retrieval capabilities of SSMs without altering their architecture.
Accounting for Work Zone Disruptions in Traffic Flow Forecasting
In particular, though state of the art performance is achieved on speed forecasting with graph neural network methods, these methods do not incorporate information on roadway maintenance work zones and their impacts on predicted traffic flows; yet, the impacts of construction work zones are of significant interest to roadway management agencies, because they translate to impacts on the local economy and public well-being.
Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms
Our investigation encompasses a wide array of techniques, including SGD and its variants, flat-minima optimizers, and new algorithms we propose under the Basin Hopping framework.
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets
The MIL-based formulation performs no worse than single instance learning on easy to moderate datasets and outperforms single-instance learning on more challenging datasets.
Multi-objective Deep Data Generation with Correlated Property Control
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.
Transformer Neural Networks Attending to Both Sequence and Structure for Protein Prediction Tasks
The increasing number of protein sequences decoded from genomes is opening up new avenues of research on linking protein sequence to function with transformer neural networks.
Interpretable Molecular Graph Generation via Monotonic Constraints
Recent advances in deep graph generative models treat molecule design as graph generation problems which provide new opportunities toward the breakthrough of this long-lasting problem.
Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks
However, a key shortcoming of state-of-the-art methods is their inability to take into account information of various modalities, for instance the impact of maintenance downtime on traffic flows.
Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithm
The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent.
Decoy Selection for Protein Structure Prediction Via Extreme Gradient Boosting and Ranking
Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity.
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning.
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function.
