Aishwarya Nambissan

[Aishwarya Nambissan @AishLearnsAI]

Dimensionality reduction results in fewer input variables which allows for a simpler predictive model that may have better performance when making predictions on new data.
#aigraph

https://t.co/4oTbUknXjk

[Aishwarya Nambissan @AishLearnsAI]

Engineers can now test models without worrying about privacy concerns since generative AI can produce large amounts of synthetic data that mirrors real-world information
#aigraph

https://t.co/ZPLTBfrkro

[Aishwarya Nambissan @AishLearnsAI]

LLMs cannot understand written text directly, so Sentence Embedding is carried out. Thanks to the large dimension of the vector created by embedding, small variations in the data can be seen with greater precision.

#aigraph
https://t.co/q6Bw1pko49

[Aishwarya Nambissan @AishLearnsAI]

Reinforcement learning with human feedback is a possible means of addressing harmful biases in language models.
#aigraph

https://t.co/VuiJdXbOsg

[Aishwarya Nambissan @AishLearnsAI]

The Deep Feature-Based Text Clustering is based on a deep pretrained model that can construct the meaning of words by contextual information, avoiding the processing of high-dimensional sparse features.
#aigraph
https://t.co/sI4hA2T2VX

[Aishwarya Nambissan @AishLearnsAI]

For easy pandas functions for plotting data by different approaches to expose interesting structures:
https://t.co/6br7j5nafu
#aigraph

[Aishwarya Nambissan @AishLearnsAI]

Hierarchical clustering has high space and time complexity. So, this clustering algorithm cannot be used for large datasets.
#aigraph
https://t.co/DM461nyV1i

[Aishwarya Nambissan @AishLearnsAI]

AI technology has the potential to improve workplace safety and efficiency by analyzing task completion patterns. Hyundai and Mercedes-Benz are using AI embedded in exoskeletons to help human workers to perform most repetitive tasks better.
#aigraph
https://t.co/PWHWBWPkxh

[Aishwarya Nambissan @AishLearnsAI]

Labeling textual data without access to gold-standard labels is a challenge. Topic modelling with some human intuition for fine tuning overcomes this challenge to quite an extent.
#aigraph
https://t.co/INHsSt3imB

[Aishwarya Nambissan @AishLearnsAI]

The memory requirements for Llama-2 does not make it a feasible option for researchers to use on their own systems. Petals is a potential alternative as it provides access to public servers that researchers can use for their LLMs.
#aigraph
https://t.co/lRNcs3uZNY

[Aishwarya Nambissan @AishLearnsAI]

Subject classification is done to help combine resources by subject, but it is done manually since it requires a lot of training. Automated subject classification techniques are now introduced:
https://t.co/vahcfiOpRc
#aigraph

[Aishwarya Nambissan @AishLearnsAI]

By providing appropriate prompts, ChatGPT can be used for data analysis and exploration, interpreting key insights, performing literature review and identifying research gaps .
#aigraph
https://t.co/iTbimk8ksT

[Aishwarya Nambissan @AishLearnsAI]

Large Language Models can be leveraged to direct clustering algorithms during semi-supervised learning, thus requiring less input from the experts.
#aigraph
https://t.co/GzqCn5oZDo

[Aishwarya Nambissan @AishLearnsAI]

CLUSTERLLM is a novel text clustering framework that takes feedback from an instruction-tuned large language model, such as ChatGPT to guide a small embedder for finding text clusters with low cost.
#aigraph
https://t.co/PGtvWmdzQR

[Aishwarya Nambissan @AishLearnsAI]

Sentence transformers are LLMs that are specially trained to output embeddings at the sentence level rather than the word or token level. These sentence level embeddings do a better job of capturing meaning and are far quicker to compute.
#aigraph
https://t.co/EjpbjyjA0j

[Aishwarya Nambissan @AishLearnsAI]

Linear models with a rank constraint and a fast loss approximation can train on a billion words within ten minutes, while achieving performance on par with the state-of-the-art for tag prediction and sentiment
analysis.
#aigraph
https://t.co/c5lalZivCQ

[Aishwarya Nambissan @AishLearnsAI]

Instruction Tuning and Reinforcement Learning with Human Feedback are 2 training steps that were applied to ChatGPT to make it work so well.
#aigraph
https://t.co/dgr7XvXzQO

[Aishwarya Nambissan @AishLearnsAI]

AIaaS constitutes a new and unexplored electronic market. They will provide new ways to learn from customer data and provide advice or instructions without being specifically programmed to do so.
#aigraph
https://t.co/uICGcGJtEK

[Aishwarya Nambissan @AishLearnsAI]

To improve the level of precision for text classification use TF-IDF method for feature extraction.
#aigraph
https://t.co/AeOLxZYBgq

[Aishwarya Nambissan @AishLearnsAI]

When the label space for describing a text is unlimited, Zero shot learning is used which involves classifying instances into categories without any labelled training data.
#aigraph
https://t.co/gGkWxdz9h0