arXiv research

Method learns multi-stage tasks from single video, overcoming challenges of raw pixel learning and insufficient demonstrations.

SDM Policy accelerates inference for robotic tasks while maintaining high action quality.

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a for…

With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides huge opportunities to improve pharmaceutical research and development. One significant application is the purpose prediction of small molecule compounds, aiming to specify therapeutic propertie…

The study improves compound selection in in silico screening by focusing on model's ability to predict desirable outcomes.

Bayesian learning improves reliability of molecular predictions for hit compound discovery.

A new method predicts compounds for orphan proteins.

The paper develops generalization bounds for deep compound Gaussian neural networks.

Reinforcement learning and planning methods require an objective or reward function that encodes the desired behavior. Yet, in practice, there is a wide range of scenarios where an objective is difficult to provide programmatically, such as tasks with visual observations involving unknown object positions or deformable…

Most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where different tasks share a set of actions. In such environments a compound policy may be learnt with shared neural network parameters, which performs multiple tasks concurrently. However such compound polic…

Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to replicate some of these abilities with a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically …

Mol-CycleGAN generates optimized molecules with similar structure.

Model predicts diverse chemical reactions for target compounds.

BBRT improves molecular properties through iterative translation.

Robot learns to control itself without rewards.

Study estimates Medallion's compounded return before fees at 31.8%.

Proposes semi-supervised feature ranking for handling high-dimensional, unlabeled data.

This review introduces graph kernels for chemoinformatics.

Characterizes measures preserving compound mixed renewal process properties.

This chapter is an attempt to present a mathematical theory of compound fractional Poisson processes. The chapter begins with the characterization of a well-known Lévy process: The compound Poisson process. The semi-Markov extension of the compound Poisson process naturally leads to the compound fractional Poisson proc…

Deep CNN model predicts neuronal cell health from images.

New model uses pretrained biochemical language models to generate drug compounds.

Simple machine learning models achieve high accuracy in toxicity prediction.

Generates natural product-like compounds using GPT models.

High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry standard B-score method, work on individual compound plates and do not exploit cross…

Develops an efficient method for compound option valuation.

Study develops advanced models to forecast complex LOB data.

Learning to control robots directly based on images is a primary challenge in robotics. However, many existing reinforcement learning approaches require iteratively obtaining millions of robot samples to learn a policy, which can take significant time. In this paper, we focus on learning a realistic world model capturi…

In this paper, we introduce a new model for the risk process based on general compound Hawkes process (GCHP) for the arrival of claims. We call it risk model based on general compound Hawkes process (RMGCHP). The Law of Large Numbers (LLN) and the Functional Central Limit Theorem (FCLT) are proved. We also study the ma…

Semi-supervised learning improves QSAR model predictions for novel compounds.

Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized comp…

Action chunking and data exploration improve behavior cloning in robotics.

Generative model learns to create molecules with multiple properties using interpretable substructures.

Compound Finance optimizes risk metrics for V3 protocol using Chainrisk simulations.

STMT predicts compounds in unknown areas with trend reflection.

New Riemannian geometry for Compound Gaussian distributions applied to efficient change detection.

ChemGrapher uses deep learning to automatically convert chemical compound images into accurate graphs.

This study deals with the problem of pricing compound options when the underlying asset follows a mixed fractional Brownian motion with jumps. An analytic formula for compound options is derived under the risk neutral measure. Then, these results are applied to value extendible options. Moreover, some special cases of …

Novel model predicts anticancer compound sensitivity with high accuracy and interpretability.

CrystalGAN generates novel stable chemical compounds using GANs.

A new method solves complex financial problems using deep learning.

Ethereum's Pectra upgrade introduces 0x02 compounding validators, offering higher stake and potential APR uplifts.

The task of multi-step ahead prediction in language models is challenging considering the discrepancy between training and testing. At test time, a language model is required to make predictions given past predictions as input, instead of the past targets that are provided during training. This difference, known as exp…

New MGCPP model for order flow in financial markets.

The understanding of the type of inhibitory interaction plays an important role in drug design. Therefore, researchers are interested to know whether a drug has competitive or non-competitive interaction to particular protein targets. Method: to analyze the interaction types we propose factorization method Macau which …

The paper introduces ESG valuation in option pricing using binomial trees.

Study predicts shear stress in compound channels using data mining and machine learning.

Paper examines GCHP for mid-price prediction in financial data.