A naukri.com initiative
Deep Learning News
Medium
2d
78
How Stylometric Analysis works part3(Machine Learning 2024)
- In this study, Japanese stylometric features of texts generated by GPT-3.5 and GPT-4 were compared to those written by humans.
- Multi-dimensional scaling (MDS) was performed to analyze the distributions of texts based on stylometric features.
- The distributions of GPT-3.5, GPT-4, and human-generated texts were found to be distinct.
- The classification performance using random forest (RF) for Japanese stylometric features showed high accuracy in distinguishing GPT-generated from human-written texts.
Read Full Article
4 Likes
Medium
2d
161
How Stylometric Analysis works part2(Machine Learning 2024)
- Large language models (LLMs) like GPT-4, PaLM, and Llama have increased the generation of AI-crafted text.
- Neural authorship attribution is a forensic effort to trace AI-generated text back to its originating LLM.
- The LLM landscape can be divided into proprietary and open-source categories.
- An empirical analysis of LLM writing signatures highlights the contrasts between proprietary and open-source models and scrutinizes variations within each group.
Read Full Article
9 Likes
Gbhackers
2d
178
ViperSoftX Malware Uses Deep Learning Model To Execute Commands
- ViperSoftX malware uses Tesseract, an open-source OCR engine, to target infected systems.
- The malware scans extracted text for passwords and cryptocurrency wallet phrases.
- ViperSoftX deploys additional malware strains like Quasar RAT and TesseractStealer.
- The malware exfiltrates image files containing sensitive information to the attacker's server.
Read Full Article
10 Likes
Medium
2d
166
Image Credit: Medium
The Role of Reinforcement Learning in Improving the Capabilities of Large Language Models
- Reinforcement learning (RL) can improve the capabilities of large language models (LLMs).
- LLMs utilize transformer architectures that are trained on vast quantities of data.
- RL is an area of machine learning that focuses on learning through interaction with an environment.
- RL can be applied to LLMs to improve their abilities in applications, such as conversational AI, content creation, and language translation.
- For example, by using RL, virtual assistants can create more contextually appropriate responses, improving their quality over time.
- RL can also be used to enhance AI content generators, producing language that is informative, accurate, and well-structured.
- To train RL agents at scale, large computing resources are required, and efficient distributed RL algorithms are essential.
- There are concerns about the ethical use of RL in language models, and researchers are examining ways to minimize biases.
- As RL research progresses, we should expect even more striking results from huge language models in the next few years.
- RL has the potential to drive the frontiers of what language models can do, with careful consideration of ethical and practical constraints.
Read Full Article
10 Likes
Fourweekmba
2d
194
Image Credit: Fourweekmba
Transformer Architecture In A Nutshell
- The transformer architecture introduced a novel approach to sequence-to-sequence tasks and language understanding that has revolutionized many areas of artificial intelligence.
- Key concepts include the self-attention mechanism, multi-head attention, and encoder-decoder architecture.
- The transformer architecture operates through several key steps including input embedding, encoder and decoder stacks processing, multi-head attention and position-wise feedforward networks.
- Transformers are suitable for various NLP and machine learning applications such as machine translation, text generation, question answering, summarization, and speech recognition.
- Challenges include significant computational demands, large and diverse datasets, and interpretability.
- The future of transformers in machine learning includes more efficient architectures, multi-modal applications, transfer learning, and ethical AI development.
- Transformers have a parallel processing and scalability feature that has tremendously helped in broading their applicability beyond NLP.
- Connected AI concepts include AGI, deep learning vs. machine learning, DevOps, AIOps, and MLOps.
- OpenAI is using the transformer models as foundational models and for creating applications on top of it.
- Stability AI, on the other hand, is working on AI products and provide AI consulting services to businesses and monetizes enterprise services.
Read Full Article
11 Likes
Medium
3d
321
Image Credit: Medium
Demystifying Deep Learning: The Brains Behind Artificial Intelligence
- Deep Learning borrows inspiration from the structure and function of the human brain.
- Deep Learning models have multiple layers of interconnected neurons, allowing them to learn intricate patterns and relationships within data.
- Deep Learning excels at tasks such as image classification using Convolutional Neural Networks.
- Despite its advantages, Deep Learning also faces challenges in areas like interpretability and data requirements.
Read Full Article
19 Likes
Medium
3d
234
Image Credit: Medium
Multi-Layer Perceptron vs Kolmogorov-Arnold Network: An Epic Showdown in the Deep Learning Arena
- Multi-Layer Perceptron (MLP) and Kolmogorov-Arnold Network (KAN) are two competing architectures in the deep learning arena.
- MLP is a versatile and widely-used neural network with input, hidden, and output layers, using activation functions to introduce non-linearity.
- KAN, based on the Kolmogorov-Arnold representation theorem, decomposes complex functions into simpler one-dimensional functions and reconstructs them.
- MLP is reliable and efficient for everyday tasks, while KAN is complex, sophisticated, and suitable for mathematical challenges.
Read Full Article
14 Likes
Medium
3d
63
Use cases of Ginzburg Landau method part3(Machine Learning)
- The Ginzburg-Landau heat flow without magnetic effect in a curved thin domain is considered.
- The weighted average of a weak solution to the thin-domain problem converges weakly on the limit surface.
- A limit equation is derived by characterizing the limit function as a weak solution.
- Difference estimates are provided for the limit surface and the curved thin domain.
Read Full Article
3 Likes
Medium
3d
262
Use cases of Ginzburg Landau method part2(Machine Learning)
- Superconducting nanowire cryotrons (nTrons) are being developed as interfaces for super-high-performance hybrid devices.
- A numerical technique using the finite element method has been developed to simulate the three-terminal operation of nTrons.
- The technique solves the time-dependent Ginzburg-Landau (TDGL) equation and the heat-diffusion equation.
- Simulation results provide insights into the dynamics, thermal behavior, and characteristics of nTrons, aiding in optimization and application development.
Read Full Article
15 Likes
Medium
3d
103
Research on Non-convex potentials part8(Machine Learning optimization)
- Study of gradient field models on an integer lattice with non-convex interactions
- Focus on strict convexity of free energy for low temperatures and small deformations
- Verification of Cauchy-Born rule for a class of models
- Application of multi-scale renormalisation group analysis techniques
Read Full Article
6 Likes
Medium
3d
47
Research on Non-convex potentials part6(Machine Learning optimization)
- Discretization of continuous-time diffusion processes is a widely recognized method for sampling.
- The Unadjusted Langevin Algorithm (ULA) is hindering the deployment of sampling methods for non-convex distributions.
- The paper introduces a new mixture weakly smooth condition and proves the convergence of ULA with additional log-Sobolev inequality.
- The paper establishes convergence guarantees for ULA using convexification of nonconvex domain and regularization.
Read Full Article
2 Likes
Towards Data Science
3d
107
Image Credit: Towards Data Science
Exploring LLMs for ICD Coding — Part 1
- Clinical coding is typically performed by human coders with medical expertise.
- The process is error-prone, slow, and bottlenecked by the requirement for significant human expertise.
- Deep learning can automate clinical coding, improving speed and accuracy and reducing billing errors.
- However, automating ICD coding is challenging due to extensive output space of labels and accurately contextualizing diagnoses in medical notes.
- LLMs demonstrate robust zero-shot and few-shot learning capabilities that can be used for relation extraction in the clinical domain.
- In a recent paper, LLM-guided tree-search was developed to identify the most pertinent ICD codes for medical notes without fine-tuning.
- The algorithm traverses the ICD tree, using LLMs to select branches for exploration and identify relevant codes.
- Implementation results are in the ballpark of reported scores in the paper, though the implementation differs in some ways.
- Utilizing LLMs as agents for clinical coding could potentially be used in workflows that analyze medical documents at a finer granularity.
- Huge thanks to Joseph, the lead author of this paper, for clarifying my doubts regarding the evaluation of this method!
Read Full Article
6 Likes
Medium
3d
167
Image Credit: Medium
Knowledge Elicitation in Educational Technology
- MAXHUB.com is a company that focuses on educational technology.
- They have a product called KESSFIRST.com which is a knowledge elicitation system scaffolds.
- KESSFIRST.com aims to gather insights from experts, users, and data to drive product excellence.
- By using scaffolded interviews, scenario mapping, user story workshops, data mining and analysis, prototyping, and iteration, MAXHUB.com ensures informed decision-making, reduces risk, and designs user-centric products.
Read Full Article
10 Likes
Hackernoon
3d
343
Image Credit: Hackernoon
Plunging Into Data: Unraveling Time Series Patterns
- Time-series data represents records of values over time and is crucial for various applications, including machine learning, natural language processing, and large language models.
- Developing effective time-series models requires a thorough understanding of key concepts and techniques, including visualization, missing value treatment, decomposition, autocorrelation analysis, and outlier detection.
- Time-series data can be decomposed into three fundamental components: trend, seasonality, and residuals.
- Visualization, such as plotting raw values and rolling averages, is a powerful tool for exploring time-series data and uncovering trends and seasonal patterns that inform modeling.
- Handling missing data is a crucial aspect of time-series analysis, and techniques like mean or time interpolation and forward/backward fill can be used to impute missing values.
- Outliers can significantly affect time-series modeling, but robust outlier detection methods like Isolation Forests can identify outlying observations while being resilient to masking and swamping effects.
- Differencing techniques like first-order and seasonal differencing can be used to make non-stationary series stationary, necessary for many time-series methods that assume stationarity.
- Mastering key concepts and techniques in time-series analysis is essential for advanced modeling and accurate forecasting, paving the way for valuable insights across domains.
Read Full Article
20 Likes
Medium
3d
27
Research on Self-Reflection for Machine Learning models part4
- Instruction tuning (IT) is crucial for tailoring large language models (LLMs) for human-centric interactions.
- A novel approach called SelectIT is proposed, which utilizes the intrinsic uncertainty of LLMs to select high-quality IT data.
- Selective Alpaca, a new IT dataset created using SelectIT and Alpaca-GPT4 dataset, demonstrates substantial enhancement in model ability.
- The robustness of SelectIT has been verified in various foundation models and domain-specific tasks.
Read Full Article
1 Like
For uninterrupted reading, download the app