arXiv research

Improves matrix multiplication throughput for asymmetric bit-width operands.

Generalised matrix-matrix multiplication forms the kernel of many mathematical algorithms. A faster matrix-matrix multiply immediately benefits these algorithms. In this paper we implement efficient matrix multiplication for large matrices using the floating point Intel Pentium SIMD (Single Instruction Multiple Data) a…

Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied in the Bayesian community, however, very little attention has been giv…

Q-NETs use neural networks to estimate integrals of low-dimensional functions efficiently.

Modified BFGS and LBFGS++ libraries boost performance for non-parallelizable functions.

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

Cryptotree enables accurate predictions on encrypted data using Random Forests.

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…

In this work, we discuss the Automatic Adjoint Differentiation (AAD) for functions of the form $G=\frac{1}{2}\sum_1^m (Ey_i-C_i)^2$, which often appear in the calibration of stochastic models. { We demonstrate that it allows a perfect SIMD\footnote{Single Input Multiple Data} parallelization and provide its relative co…

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 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…

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

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

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

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

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

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

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…

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.

Framework learns useful subgoals from demonstrations and instructions.

WebGUM learns web navigation from multimodal data, outperforming previous methods.

Training-free looped transformers improve model performance without additional training.

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

In2Core selects a coreset for efficient LLM fine-tuning with reduced data.

Qwen3-8B outperforms classical models in financial text classification.

BTZSC benchmarks zero-shot text classification across diverse models.

New models avoid lookahead bias by training on past data only.

We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this i…

Study reveals LLM personality patterns but lacks behavioral consistency.

Study shows multilingual LLM calibration effects improve model confidence but not accuracy.

This paper evaluates quantization techniques for deep learning inference.

Combining watermark and non-watermark detectors improves LLM detection.

Advanced AI model predicts stock movements post earnings reports.

THERMOMETER calibrates LLMs efficiently for diverse tasks.

SAGE enhances reinforcement learning by injecting hints to prevent model stagnation.

CoE modularizes LLMs for scalable, cost-effective AI systems.

Our community has greatly improved the efficiency of deep learning applications, including by exploiting sparsity in inputs. Most of that work, though, is for inference, where weight sparsity is known statically, and/or for specialized hardware. We propose a scheme to leverage dynamic sparsity during training. In parti…

Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and un…