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Marktechpost
1d
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Sequential-NIAH: A Benchmark for Evaluating LLMs in Extracting Sequential Information from Long Texts
- Evaluating how well LLMs handle long contexts is essential, especially for retrieving specific, relevant information embedded in lengthy inputs.
- Needle-in-a-Haystack (NIAH) task challenges models to retrieve critical information from predominantly irrelevant content and lacks tasks involving retrieval and correct ordering of sequential information.
- Sequential-NIAH benchmark designed to assess how well LLMs retrieve sequential information, referred to as a needle, from long texts.
- Tests on popular LLMs showed highest performance at just 63.15%, highlighting the difficulty of the task and need for further advancement.
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Medium
1d
572
Image Credit: Medium
Why AI Still Can’t Get Sarcasm ?
- AI struggles to comprehend sarcasm as it cannot understand the subtextual cues and contextual shifts.
- Sarcasm requires understanding the intention behind the words, which is challenging for AI.
- AI language models may improve in detecting tone and even being sarcastic, but they will never experience true empathy.
- Sarcasm remains a human tool for social commentary and a defense mechanism that machines cannot fully replicate.
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Mit
2d
237
Image Credit: Mit
Robotic system zeroes in on objects most relevant for helping humans
- MIT roboticists have developed a system, called "Relevance," to help robots focus on relevant features in a scene for assisting humans.
- The Relevance approach enables robots to determine a human's objective using cues like audio and visual information.
- A robot can then identify objects most likely to be relevant in fulfilling the human's objective and act accordingly.
- In an experiment simulating a conference breakfast buffet, the robot successfully assisted humans in various scenarios with high accuracy.
- The robot predicted a human's objective with 90% accuracy and identified relevant objects with 96% accuracy.
- This method not only improves a robot's efficiency but also enhances safety by reducing collisions by over 60%.
- The system mimics the human brain's Reticular Activating System to selectively process and filter information.
- It consists of phases like perception, trigger check, relevance determination, and object offering based on relevance.
- The researchers aim to apply this system in smart manufacturing, warehouse environments, and household tasks for more natural human-robot interactions.
- The team's goal is to enable robots to offer seamless, intelligent, safe, and efficient assistance in dynamic environments.
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Arxiv
2d
120
Image Credit: Arxiv
Representation Learning for Tabular Data: A Comprehensive Survey
- Tabular data is prevalent in machine learning classification and regression applications.
- Deep Neural Networks (DNNs) are promising for representation learning in tabular data.
- Existing models for tabular representation learning are categorized into specialized, transferable, and general models.
- The survey also covers ensemble methods and extensions of tabular learning.
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Arxiv
2d
82
Image Credit: Arxiv
Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement
- This paper gives an overview of Active Learning (AL) in machine learning, which helps models achieve better performance using fewer labeled examples.
- AL is used in various fields such as computer vision, natural language processing, transfer learning, and real-world applications.
- The paper focuses on topics like uncertainty estimation, handling class imbalance, fairness, and creation of strong evaluation metrics.
- AL often gives better results than passive learning, especially with good evaluation measures.
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Arxiv
2d
179
Image Credit: Arxiv
SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures
- SparseJEPA is an extension of Joint Embedding Predictive Architectures (JEPA) that integrates sparse representation learning to enhance the quality of learned representations.
- SparseJEPA encourages shared latent space variables among data features with strong semantic relationships while maintaining predictive performance.
- The architecture was tested on the CIFAR-100 dataset and a lightweight Vision Transformer for image classification and low-level tasks using transfer learning.
- Incorporating sparsity improves the latent space, leading to more meaningful and interpretable representations.
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Arxiv
2d
182
Image Credit: Arxiv
Deep Learning Meets Process-Based Models: A Hybrid Approach to Agricultural Challenges
- Process-based models (PBMs) and deep learning (DL) are two key approaches in agricultural modelling.
- This study presents a review of PBMs, DL models, and hybrid PBM-DL frameworks in agricultural modelling.
- Results demonstrate that hybrid models consistently outperform traditional PBMs and DL models.
- The study contributes to the development of scalable, interpretable, and reproducible agricultural models for sustainable agriculture.
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Arxiv
2d
70
Image Credit: Arxiv
Hexcute: A Tile-based Programming Language with Automatic Layout and Task-Mapping Synthesis
- Hexcute is a tile-based programming language designed for deep learning workloads.
- It exposes shared memory and register abstractions to enable fine-grained optimization.
- Hexcute automates layout and task mapping synthesis with a type-inference-based algorithm.
- Evaluation shows that Hexcute achieves significant speedup over existing DL compilers.
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Arxiv
2d
326
Image Credit: Arxiv
General Post-Processing Framework for Fairness Adjustment of Machine Learning Models
- A new framework for fairness adjustments in machine learning models has been introduced in a recent paper on arXiv.
- The framework applies to various machine learning tasks, including regression and classification, and supports different fairness metrics.
- Unlike traditional approaches, this method adapts in-processing techniques for post-processing, providing greater flexibility in model development.
- The advantages of this framework include preserving model performance, eliminating the need for custom loss functions, accommodating black-box systems, and providing interpretable insights.
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Arxiv
2d
167
Image Credit: Arxiv
FairPlay: A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness
- FairPlay is a web-based software application that enables multiple stakeholders to collaboratively debias datasets.
- Users can negotiate and arrive at a mutually acceptable outcome without a universally agreed-upon theory of fairness.
- Through user studies, FairPlay has demonstrated the ability to help users reach a consensus within about five rounds of gameplay.
- The application has the potential to enhance fairness in AI systems by addressing the issue of bias in datasets.
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Arxiv
2d
276
Image Credit: Arxiv
Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matching
- Energy-based models (EBMs) are a powerful class of probabilistic generative models.
- Variational Potential Flow Bayes (VPFB) is a new energy-based generative framework that eliminates the need for implicit MCMC sampling.
- VPFB learns an energy-parameterized potential flow by matching a flow-driven density homotopy to the data distribution.
- Experimental results show that VPFB performs competitively with existing approaches in terms of sample quality and versatility.
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Arxiv
2d
281
Image Credit: Arxiv
Gradient-Optimized Fuzzy Classifier: A Benchmark Study Against State-of-the-Art Models
- A benchmark study was conducted to compare the performance of a Gradient-Optimized Fuzzy Inference System (GF) classifier against various state-of-the-art machine learning models.
- The evaluation was done on five datasets from the UCI Machine Learning Repository, each offering diverse input types, class distributions, and classification complexity.
- Compared to traditional Fuzzy Inference Systems, the GF classifier, which utilizes gradient descent, demonstrated superior classification accuracy, high precision, and significantly lower training times.
- The GF model exhibited consistency across folds and datasets, indicating its robustness in handling noisy data and varying feature sets.
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Arxiv
2d
299
Image Credit: Arxiv
Boosting Classifier Performance with Opposition-Based Data Transformation
- A novel data transformation framework based on Opposition-Based Learning (OBL) is introduced to boost the performance of traditional classification algorithms.
- OBL generates synthetic opposite samples to replace the training data and improve decision boundary formation.
- Three OBL variants, Global OBL, Class-Wise OBL, and Localized Class-Wise OBL, are explored and integrated with popular classifiers.
- Experiments show that OBL-enhanced classifiers consistently outperform standard counterparts in terms of accuracy and F1-score, particularly in complex or sparse learning environments.
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Arxiv
2d
7
Image Credit: Arxiv
Learning Explainable Dense Reward Shapes via Bayesian Optimization
- Current reinforcement learning from human feedback (RLHF) pipelines for large language model (LLM) alignment typically assign scalar rewards to sequences, using the final token as a surrogate indicator for the quality of the entire sequence.
- This work proposes a reward-shaping function that leverages explainability methods such as SHAP and LIME to estimate per-token rewards from the reward model.
- The study utilizes a bilevel optimization framework that integrates Bayesian Optimization and policy training to handle noise from token reward estimates.
- Experiments demonstrate that achieving a better balance of token-level reward attribution leads to improved performance and faster training of the optimal policy.
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Arxiv
2d
19
Image Credit: Arxiv
Quantum Doubly Stochastic Transformers
- Researchers have developed a hybrid classical-quantum doubly stochastic Transformer (QDSFormer) which incorporates a variational quantum circuit in place of the Softmax in the self-attention layer.
- The QDSFormer yields more diverse doubly stochastic matrices (DSMs) that better preserve information compared to classical operators.
- In multiple small-scale object recognition tasks, the QDSFormer outperforms a standard Vision Transformer and other doubly stochastic Transformers.
- The QDSFormer shows improved training stability and lower performance variation, potentially mitigating the unstable training of Vision Transformers on small-scale data.
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