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Found 13 papers, 4 papers with code
Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
The application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the telecommunication domain presents unique challenges, primarily due to the complex nature of telecom standard documents and the rapid evolution of the field.
Telecom Language Models: Must They Be Large?
The increasing interest in Large Language Models (LLMs) within the telecommunications sector underscores their potential to revolutionize operational efficiency.
FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments
As deep learning models become increasingly large, they pose significant challenges in heterogeneous devices environments.
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
Afterwards, using the provided dataset, an evaluation is conducted to assess the capabilities of LLMs, including GPT-3. 5 and GPT-4.
Large Language Models for Telecom: Forthcoming Impact on the Industry
Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force, revolutionizing fields well beyond Natural Language Processing (NLP) and garnering unprecedented attention.
Data pruning and neural scaling laws: fundamental limitations of score-based algorithms
Data pruning algorithms are commonly used to reduce the memory and computational cost of the optimization process.
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
We consider the optimisation of large and shallow neural networks via gradient flow, where the output of each hidden node is scaled by some positive parameter.
Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks
In this article, we advocate for the design of ad hoc artificial intelligence (AI)/machine learning (ML) models to facilitate their usage in future smart infrastructures based on communication networks.
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
Under this model, we show that each layer of the infinite-width neural network can be characterised by two simple quantities: a non-negative scalar parameter and a L\'evy measure on the positive reals.
Regularization in ResNet with Stochastic Depth
Regularization plays a major role in modern deep learning.
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series.
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior
Bayesian nonparametric approaches, in particular the Pitman-Yor process and the associated two-parameter Chinese Restaurant process, have been successfully used in applications where the data exhibit a power-law behavior.
On consistent estimation of the missing mass
Given $n$ samples from a population of individuals belonging to different types with unknown proportions, how do we estimate the probability of discovering a new type at the $(n+1)$-th draw?
