arXiv research

Efficient FPGA virtualization for deep learning reduces user isolation and overhead.

We design a dispatch system to improve the peak service quality of video on demand (VOD). Our system predicts the hot videos during the peak hours of the next day based on the historical requests, and dispatches to the content delivery networks (CDNs) at the previous off-peak time. In order to scale to billions of vide…

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of unc…

Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be ins…

Proposes a machine learning framework for more efficient economic dispatch.

Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed world, is moving towards on-demand manufacturing meaning a high mix, low volume product mix. This requires efficient dispatching that can wor…

Modeling intraday dispatch for wind-battery assets to meet grid targets.

Method constructs finance LLMs without instruction data using pretraining and model merging.

Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace. Hand-crafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be…

DiffOPF solves multi-valued OPF problems by sampling from system history.

Sparse oblique decision tree improves security rules for renewable power systems.

Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction. Final goals are specified to our agent via images o…

Paper proposes MAMRL for efficient energy dispatch in self-powered edge computing systems.

New approach uses deep reinforcement learning for vehicle dispatching, reducing waiting times.

Deep RL learns effective job shop scheduling rules from raw features.

Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent's learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of…

Interactive learning with hindsight instruction feedback achieves better performance than traditional methods.

In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou 1993), we propo…

In this paper, we propose a framework that enables a human teacher to shape a robot behaviour by interactively providing it with unlabeled instructions. We ground the meaning of instruction signals in the task-learning process, and use them simultaneously for guiding the latter. We implement our framework as a modular …

A method to improve LLMs by automating the construction of a mixture of expert prompts.

Improved financial sentiment analysis using simple instruction tuning of LLMs.

Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. While these characterizations may foster instructing, conditioning or structuring interactive agent behavior, it remains an open-problem to correctly relate language underst…

The energy transition is well underway in most European countries. It has a growing impact on electric power systems as it dramatically modifies the way electricity is produced. To ensure a safe and smooth transition towards a pan-European electricity production dominated by renewable sources, it is of paramount import…

Improves matrix multiplication throughput for asymmetric bit-width operands.

New findings challenge the importance of forecast accuracy in battery storage optimization, highlighting the role of rank correlation instead.

Paper fine-tunes LLaMA-3-8B for financial NER using instruction and LoRA.

Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This paper focuses on deep reinforcement learning (DRL)-based…

RAG-IT automates financial analysis using LLMs and specialized datasets.

DNN policies improve stochastic AC OPF for power grid optimization.

Study shows model size inversely affects few-shot instruction accuracy.

Enhances LLMs for predicting stock movements by considering news dissemination and context.

A learning-based algorithm optimizes admission control in a queuing system.

Improved forecast accuracy for energy systems through decision-focused fine-tuning.

We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with theaim of increasing security and reducing costs. Power networks are highly regulated systems, which at all times must meet varying demands of electricity with a complex production sys…

Improved robot navigation using multi-head attention for natural language instructions.

This paper benchmarks FinGPT for financial datasets using open-source large language models.

Paper benchmarks and customizes energy forecasting methods.

This paper proposes a new method to connect language and physical actions in reinforcement learning.

LLM Pro Finance Suite enhances financial NLP with instruction-tuned models.

SymNoise improves language model fine-tuning by 6.7% over NEFTune, using symmetric noise.

InvestLM is a financial domain LLM tuned on LLaMA-65B for investment advice.

Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers. Building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC…

GP CC-OPF solves uncertain power grid optimization with Gaussian Process.

Proposes a new method for predicting uncertain net electricity demand.

Human communication takes many forms, including speech, text and instructional videos. It typically has an underlying structure, with a starting point, ending, and certain objective steps between them. In this paper, we consider instructional videos where there are tens of millions of them on the Internet. We propose a…

While traditional methods for instruction-following typically assume prior linguistic and perceptual knowledge, many recent works in reinforcement learning (RL) have proposed learning policies end-to-end, typically by training neural networks to map joint representations of observations and instructions directly to act…

Neuro-symbolic agent learns systematic generalisation from formal instructions.

A new method solves complex hydroelectricity planning problems.