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Found 15 papers, 1 papers with code
Evaluation of Table Representations to Answer Questions from Tables in Documents : A Case Study using 3GPP Specifications
The major challenge in this is that unlike free-flow text or isolated set of tables, the representation of a table in terms of what is a relevant chunk is not obvious.
Icing on the Cake: Automatic Code Summarization at Ericsson
Notably, one of our simpler approaches performed as well as or better than the ASAP method on both the Ericsson project and the open-source projects.
Evaluation of RAG Metrics for Question Answering in the Telecom Domain
Next, we analyse the expert evaluations of the output of the modified RAGAS package and observe the challenges of using it in the telecom domain.
Towards Understanding Domain Adapted Sentence Embeddings for Document Retrieval
Our experiments establish that the isotropy of embeddings (as measured by two independent state-of-the-art isotropy metric definitions) is poorly correlated with retrieval performance.
Observations on Building RAG Systems for Technical Documents
Retrieval augmented generation (RAG) for technical documents creates challenges as embeddings do not often capture domain information.
Observations on LLMs for Telecom Domain: Capabilities and Limitations
The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3. 5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others.
Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images
We show that popular choices of dataset selection suffer from data homogeneity, leading to misleading results.
Complexity Controlled Generative Adversarial Networks
One of the issues faced in training Generative Adversarial Nets (GANs) and their variants is the problem of mode collapse, wherein the training stability in terms of the generative loss increases as more training data is used.
An Online Learning Approach for Dengue Fever Classification
This paper introduces a novel approach for dengue fever classification based on online learning paradigms.
Effect of Various Regularizers on Model Complexities of Neural Networks in Presence of Input Noise
We show that $L_2$ regularization leads to a simpler hypothesis class and better generalization followed by DARC1 regularizer, both for shallow as well as deeper architectures.
Radius-margin bounds for deep neural networks
Explaining the unreasonable effectiveness of deep learning has eluded researchers around the globe.
Learning Neural Network Classifiers with Low Model Complexity
Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
Scalable Twin Neural Networks for Classification of Unbalanced Data
In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets.
Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACO$_\mathbb{R}$
This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization ($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library.
A Neurodynamical System for finding a Minimal VC Dimension Classifier
The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an exact bound on the Vapnik-Chervonenkis (VC) dimension.
