Large language model
Google DeepMind’s DiLoCo is a new optimization method for training language models that greatly reduces the need for communication, handles device differences, and maintains high performance. Inspired by Federated Learning, it incorporates AdamW and Nesterov Momentum, and works by synchronizing models across devices less frequently. DiLoCo demonstrated robust results with the C4 dataset, matching synchronous…
Optimization Algorithms (OA) excel at exploiting patterns; Machine Learning (ML) excels at detecting them. Instead of competition, integrating OA’s structure-exploiting abilities with ML’s pattern-detection capabilities can enhance performance. This synergy can produce more efficient, tailored solutions and has emerged as a growing research field with real-world applications.
A study from 2020 to 2023 compared the output of GPT models (GPT-2, GPT-3.5, and GPT-4) on job associations with gender, race, and political ideology. It found evolving biases: GPT-4 associated ‘software engineer’ with women and showed political polarization in job associations. Shifts in gender-neutral occupations and increased alignment with certain religions in occupational roles…
Facial Emotion Recognition (FER) is crucial for improved human-machine interaction. Advances have shifted from manual feature extraction to deep learning models like CNNs and Vision Transformer models. A recent paper tackled FER challenges by developing a balanced dataset (FER2013_balanced), which enhanced the accuracy of transformer-based models, underscoring the importance of dataset quality for FER systems.
Game Theory is a mathematical field that can assist in everyday decision-making by modeling interactions and outcomes between agents. It can predict behaviors and identify strategies when outcomes depend on others’ choices, like choosing dinner with friends or purchasing a protection plan. Understanding Game Theory concepts like Nash Equilibrium can apply to scenarios from alien…
To prevent overfitting in neural networks, regularize by applying L1 (Lasso) and L2 (Ridge) penalties to loss functions, using early stopping based on validation set performance, implementing dropout, simplifying the architecture, gathering more data, and augmenting datasets. Key methods recommended are early stopping and dropout.
Plotly enables creating animated plots, adding dynamism to the visuals, and capturing audience attention. By reshaping data to create animation frames, one can emphasize key aspects and build anticipation. Though Plotly lacks direct animation export, workarounds like screen-capture GIFs are possible. Enhanced animated plots can significantly improve the presentation’s impact.
UC Berkeley researchers have developed RLIF, a reinforcement learning method that integrates user interventions as rewards. It outperforms other models, notably with suboptimal experts, in high-dimensional and real-world tasks. RLIF’s theoretical analysis addresses the suboptimality gap and sample complexity, offering a practical alternative in learning-based control without assuming optimal human expertise. Future work will focus…
Large Language Models (LLMs) must judge textual qualities consistently for reliability. Inconsistency in evaluations leads to untrustworthy results. Universal Self-Consistency (USC) improves LLM consistency across diverse tasks. Integrating external knowledge increases reasoning accuracy. Seeded sampling aids determinism, enhancing reliability. Contrastive-consistent ranking (CCR) ensures logical consistency in model rankings. A retrieval-augmented generation system (RAG) paired with…
This pet project for Data/Analytics Engineers involves using dbt Core, Snowflake, Fivetran, and GitHub Actions to build an end-to-end data lifecycle from Google Calendar to Snowflake Dashboard. It includes steps for data extraction, transformation, storage, and visualization, offering a practical experience with modern data stack tools.
BigQuery’s GENERATE_TEXT function enables SQL-oriented data professionals to conduct NLP tasks like sentiment analysis and entity extraction in BigQuery. It uses Vertex AI’s LLM and requires knowledge of SQL and prompt structuring. The function supports various tasks and accommodates varied responses through parameters like temperature, max_output_tokens, top_k, and top_p. The post includes a hands-on guide…
Static workload benchmarks are insufficient for evaluating ANN indexes in vector databases because they focus only on recall and query performance, overlooking crucial aspects like indexing performance and memory usage. The author advocates for streaming workload benchmarks, showcasing new insights into recall stability and performance by comparing HNSWLIB and DiskANN under a streaming workload. The…
The article discusses whether the Transformer, a dominant AI model, will continue to lead or be replaced. Transformers are effective in various AI subdomains but face challenges like computational costs and data volume requirements. Industry bureaucracy slows down innovation while open-source rapidly progresses. The transformer’s dominance may be challenged by new models capable of in-context…
The proposed adaptive weight decay method automatically adjusts the weight decay hyper-parameter during training to improve adversarial robustness and counter robust overfitting, without needing extra data, by dynamically basing it on classification and regularization loss gradients.
Researchers from KAIST developed Quatro++, which improves LiDAR SLAM by tackling sparsity and degeneracy through ground segmentation. It achieves better loop closing, precise mappings, and outperforms learning-based methods. Quatro++ enhances robust registration for ground vehicles and shows high success on the KITTI dataset, making it highly effective and versatile for both LiDAR and INS systems.
Researchers introduced a Physics-informed deep learning model to predict intratumoral fluid pressure and liposome accumulation, enhancing cancer treatment strategies. The model aims for accurate drug distribution insights, addressing inconsistencies in existing nanotherapeutic approaches and improving personalized therapy design. This marks a significant advancement in understanding tumor dynamics.
This paper introduces a versatile multimodal training scheme named 4M, which uses a unified Transformer encoder-decoder to handle various input/output modalities such as text, images, and semantic data, aiming to achieve a broad functionality similar to large language models in computer vision.
Apple is sponsoring the in-person NeurIPS conference in New Orleans from December 10-16, fostering research exchange on neural information processing in various disciplines. The summary doesn’t include Apple’s specific workshop and event schedules.
AWS’s suite of low-code and no-code ML tools, such as Amazon SageMaker Canvas, enables rapid, cost-effective machine learning model development without requiring coding expertise. Deloitte uses these tools to expedite project delivery and take on more clients, increasing accessibility and standardization while reducing time and costs, resulting in roughly 30-40% productivity gains in ML development…
As an analyst, to make impactful product changes, follow best practices and insights shared in the detailed guide available on the “Towards Data Science” platform.