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Arxiv
1d
34
Image Credit: Arxiv
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
- Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion.
- URMA (Unified Robot Morphology Architecture) is introduced as a framework to control different embodiments of legged robots.
- The framework utilizes an end-to-end Multi-Task Reinforcement Learning approach and morphology-agnostic encoders and decoders.
- Experiments show that URMA can learn a locomotion policy that can be transferred to unseen robot platforms in simulation and the real world.
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Arxiv
1d
131
Image Credit: Arxiv
Privacy Vulnerabilities in Marginals-based Synthetic Data
- Privacy vulnerabilities have been identified in marginals-based synthetic data generation.
- Marginals-based synthetic data generation algorithms leak information about individuals that can be recovered more efficiently than previously understood.
- A membership inference attack, MAMA-MIA, has been developed to exploit these vulnerabilities.
- The attack allows for more accurate and faster learning of hidden data compared to other leading attacks.
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Arxiv
1d
34
Image Credit: Arxiv
Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective
- Neighbor embedding methods, such as t-SNE and UMAP, are widely used for visualizing high-dimensional data.
- A lack of data-independent notions of embedding maps in these methods can introduce misleading visual artifacts.
- Researchers have introduced LOO-map, a framework that extends embedding maps to the entire input space, aiming to improve reliability.
- Two types of diagnostic scores have been developed to detect unreliable embedding points and improve hyperparameter selection.
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Arxiv
1d
81
Image Credit: Arxiv
1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization
- A learning-based approach is proposed to synthesize policies for huge parameterized Markov decision processes (MDPs).
- The method generalizes optimal policies obtained from model-checking small instances to larger ones using decision-tree learning.
- By bypassing the need for explicit state-space exploration of large models, the method provides a practical solution to the state-space explosion problem.
- Experimental results show that the policies perform well even for models beyond the reach of state-of-the-art analysis tools.
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Arxiv
1d
135
Image Credit: Arxiv
Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks
- Researchers present a mathematical model for predicting the growth of glioblastoma (GBL) and identifying patient-specific parameters from neuroimaging data.
- The model utilizes a diffuse-interface mathematical model and a reduced-order modeling strategy trained on synthetic data derived from patient-specific brain anatomies reconstructed from imaging.
- A neural network surrogate learns the inverse mapping from tumor evolution to model parameters, resulting in significant computational speed-up while maintaining high accuracy.
- The study establishes a foundation for the development of patient-specific digital twins in neuro-oncology for future clinical applications.
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Arxiv
1d
96
Image Credit: Arxiv
Multi-objective Combinatorial Methodology for Nuclear Reactor Site Assessment: A Case Study for the United States
- As clean energy demand grows, repurposing coal power plant sites (CPP) with existing infrastructure is one way to reduce costs for nuclear power plants (NPP).
- A multi-objective optimization methodology, using combinatorial search, evaluated over 30,000 potential NPP sites in the United States.
- The methodology generated a comprehensive database of site locations, attributes, site scores, and the contribution of each attribute to the score.
- Results indicate that CPP sites in Ohio, North Carolina, and New Hampshire, as well as Brownfield sites in Florida and California, are promising locations for nuclear development.
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Arxiv
1d
173
Image Credit: Arxiv
Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales
- Researchers propose a method for fully quantized Winograd convolution to reduce computational and storage costs in large-scale text-to-image diffusion models.
- Quantization of diffusion models has been explored in previous works to reduce compute costs and memory bandwidth usage.
- The proposed method focuses on finer-grained group-wise quantization, combined with finetuning the scale parameters of the Winograd transform matrices.
- The method achieves near-lossless quality in text-to-image generation and outperforms state-of-the-art methods in image classification tasks.
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Arxiv
1d
135
ZETA: Leveraging Z-order Curves for Efficient Top-k Attention
- The Transformer model, widely used for sequence modeling, relies on self-attention which becomes inefficient for long sequences.
- The ZETA method proposes leveraging Z-Order Curves for Efficient Top-k Attention to address the inefficiency of self-attention.
- ZETA enables parallel querying of past tokens and achieves similar performance to self-attention while reducing computational demands.
- Experimental results show that ZETA outperforms attention models on various language modeling tasks.
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Medium
1d
306
Top 5 Books to Learn AI: In My Point of View
- 1. 'Artificial Intelligence: A Modern Approach' by Russell and Norvig is the go-to guide for understanding AI fundamentals.
- 2. 'Deep Learning' by Goodfellow, Bengio, and Courville explains the mechanics of neural networks and their applications.
- 3. 'The Master Algorithm' by Pedro Domingos explores the concept of a universal algorithm for deriving knowledge from data.
- 4. 'Superintelligence' by Nick Bostrom delves into the risks and rewards of advanced AI and its alignment with human goals.
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Medium
1d
154
Image Credit: Medium
How to Sound Like a Good Writer?
- Refine your writing style by using examples to illustrate each point.
- Develop a distinct voice by using language that sets you apart.
- Inject personality by incorporating humor, rhetorical questions, or storytelling.
- Avoid monotony by varying sentence structure and tone.
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Towards Data Science
1d
241
The Case for Centralized AI Model Inference Serving
- AI models are increasingly being used in algorithmic pipelines, leading to different resource requirements compared to traditional algorithms.
- Efficiently processing large-scale inputs with deep learning models can be challenging within these pipelines.
- Centralized inference serving, where a dedicated server handles prediction requests from parallel jobs, is proposed as a solution.
- An experiment comparing decentralized and centralized inference approaches using a ResNet-152 image classifier on 1,000 images is conducted.
- The experiment focuses on Python multiprocessing for parallel processing on a single node.
- Centralized inference using a dedicated server showed improved performance and resource utilization compared to decentralized inference.
- Further enhancements and optimizations can be made, including custom inference handlers, advanced server configurations, and model optimization.
- Batch inference and multi-worker inference strategies are explored to improve throughput and resource utilization.
- Results show that utilizing an inference server can significantly boost overall throughput and efficiency in deep learning workloads.
- Optimizing AI model execution involves designing efficient inference serving architectures and considering various model optimization techniques.
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Medium
1d
352
Image Credit: Medium
Robustness in Optimal Transport Theory: Building Reliable AI Models
- Robustness in optimal transport theory focuses on creating AI models that perform reliably even when faced with different data, noise, changing conditions, or limited information.
- It is crucial for AI systems in critical areas like healthcare, transportation, and finance to ensure reliability when faced with unexpected scenarios.
- Optimal transport theory deals with efficiently moving resources while minimizing costs, often involving comparing and transforming probability distributions in AI.
- Robustness is necessary due to data noise, changing environments, and discrepancies between training and real-world data in machine learning models.
- Adapting to unexpected scenarios is a key aspect of robustness, such as optimizing delivery routes accounting for disruptions like road construction.
- The robust Wasserstein distance is a measure of maximum possible distance between distributions in uncertainty sets, aiding in conservative estimates for robustness.
- DRO (Distributionally Robust Optimization) optimizes AI model parameters for worst-case expected loss across various data distributions to enhance robustness.
- Entropy regularization and data augmentation are common techniques used to improve robustness in optimal transport problems by smoothing solutions and introducing variations in training data.
- Robust optimal transport helps AI models perform consistently against adversarial examples, improve generalization across domains, and create more stable generative models in deep learning.
- Practical approaches to evaluate the robustness of AI models include exposing them to challenging conditions, quantifying robustness using metrics like worst-case accuracy, and testing performance under distribution shifts.
- The reliability and robustness provided by optimal transport theory play a critical role in building AI systems that can be trusted in crucial domains with real-world uncertainties.
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Medium
1d
143
Image Credit: Medium
How Close Are We to AGI?
- Artificial General Intelligence (AGI) is the ultimate goal of AI research, aiming to create machines that can think, reason, and understand like humans.
- Current AI can mimic intelligence but lacks true understanding, reasoning, and adaptability that humans possess.
- AGI would require significant advancements in processing capabilities and overcoming challenges related to control, moral alignment, and computational resources.
- The development of AGI remains uncertain, with some researchers providing optimistic timelines while others express doubts about its feasibility.
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Medium
1d
19
Image Credit: Medium
Optimal Transport Theory: From Mathematical Concepts to Real-World Applications
- Optimal transport theory tackles efficient resource movement from sources to destinations, utilizing mathematical frameworks to minimize costs.
- Real-world applications, like goods delivery and resource allocation, benefit from optimal transport theory's systematic approach.
- Game-based examples like the Candy Delivery Game illustrate how mathematical concepts optimize practical resource allocation problems.
- Using cost matrices, optimal paths can be determined by minimizing total transport costs in scenarios like candy delivery mazes.
- The Apple Distribution Game introduces capacity constraints, mirroring real-world resource allocation challenges.
- Mathematically, optimal transport problems aim to minimize total transport costs while ensuring resources reach their destinations efficiently.
- Leonid Kantorovich's linear programming reformulation in the 1940s made optimal transport problems more solvable in varied settings.
- Applications of optimal transport theory span supply chain optimization, market equilibrium, and image processing in diverse fields.
- Real-world applications may involve factors like varying costs, time constraints, and uncertain conditions, addressed by robust optimal transport solutions.
- Computational solutions for optimal transport problems often involve linear programming or specialized algorithms for efficiency in diverse scenarios.
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Medium
1d
372
Image Credit: Medium
Beyond Reactive Chatbots
- The article discusses the trade-off between speed and depth in AI-driven chatbots and explores a solution using a tiered reasoning approach inspired by human cognition.
- A practical architectural framework is introduced to create conversational AI systems that think fast, think deep, and evolve over time.
- The dual-process theory, involving fast intuition and slow deliberation, serves as the basis for structuring AI processing into distinct layers.
- System 1 focuses on fast thinking, providing immediate responses based on prompt information and short-term memory, while System 2 handles deeper, asynchronous processing.
- Implementing System 2 involves using tools like Celery for asynchronous task execution to balance responsiveness with deeper analysis.
- System 3 operates offline, processing historical data to enhance future interactions and allowing the AI to learn and evolve over time.
- The tiered reasoning approach is demonstrated through industry-specific challenges in areas like financial analysis, technical diagnostics, and schedule optimization.
- By balancing responsiveness and deep analysis, this architecture creates AI assistants that are both thoughtful and adaptive, inspired by human cognitive processes.
- As AI assistants powered by LLMs become more prevalent, the ability to blend immediate engagement with deeper reasoning will differentiate valuable assistants from reactive chatbots.
- The article encourages sharing of experiences in implementing tiered reasoning approaches for better conversational AI to advance beyond reactive chatbots.
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