Meet Beepo-22B: The Unrestricted AI Finetuned Model based on Mistral Small Instruct 22B Asif Razzaq Artificial Intelligence Category – MarkTechPost

[[{“value”:” Modern language models have transformed our daily interactions with technology, offering tools that help draft emails, write articles, code software, and much more. However, these powerful models often come with significant limitations. Many language models today are hamstrung by overly cautious guardrails that restrict… Read More »Meet Beepo-22B: The Unrestricted AI Finetuned Model based on Mistral Small Instruct 22B Asif Razzaq Artificial Intelligence Category – MarkTechPost

Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning Apple Machine Learning Research

[[{“value”:”This paper was accepted at the Self-Supervised Learning – Theory and Practice (SSLTP) Workshop at NeurIPS 2024. Image-based Joint-Embedding Predictive Architecture (IJEPA) offers an attractive alternative to Masked Autoencoder (MAE) for representation learning using the Masked Image Modeling framework. IJEPA drives representations to capture useful… Read More »Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning Apple Machine Learning Research

This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting, a strong case of out-of-distribution generalization. This is motivated by the fact that the rich combinatorial nature of data in certain reasoning tasks (e.g., arithmetic/logic)… Read More »Generalization on the Unseen, Logic Reasoning and Degree Curriculum Apple Machine Learning Research

[[{“value”:”This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) Workshop at NeurIPS 2024. Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture… Read More »Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models Apple Machine Learning Research

We present Recurrent Drafter (ReDrafter), an advanced speculative decoding approach that achieves state-of-the-art speedup for large language models (LLMs) inference. The performance gains are driven by three key aspects: (1) leveraging a recurrent neural network (RNN) as the draft model conditioning on LLM’s hidden states,… Read More »Recurrent Drafter for Fast Speculative Decoding in Large Language Models Apple Machine Learning Research

[[{“value”:” Artificial intelligence systems often struggle with retaining meaningful context over extended interactions. This limitation poses challenges for applications such as chatbots and virtual assistants, where maintaining a coherent conversation thread is essential. Most traditional AI models operate in a stateless manner, focusing solely on… Read More »Meet Memoripy: A Python Library that Brings Real Memory Capabilities to AI Applications Asif Razzaq Artificial Intelligence Category – MarkTechPost

[[{“value”:” Developments in simulating particulate flows have significantly impacted industries ranging from mining to pharmaceuticals. Particulate systems consist of granular materials interacting with each other and surrounding fluids, and their accurate modeling is critical for optimizing processes. However, traditional numerical methods like the Discrete Element… Read More »NeuralDEM: Pioneering High-Performance Simulation of Large-Scale Particulate Systems with Multi-Branch Neural Operator Architectures Nikhil Artificial Intelligence Category – MarkTechPost

[[{“value”:” Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse applications, but their widespread adoption faces significant challenges. The primary concern stems from training datasets that contain varied, unfocused, and potentially harmful content, including malicious code and cyberattack-related information. This creates a critical need… Read More »H-DPO: Advancing Language Model Alignment through Entropy Control Mohammad Asjad Artificial Intelligence Category – MarkTechPost

[[{“value”:” Multi-label text classification (MLTC) assigns multiple relevant labels to a text. While deep learning models have achieved state-of-the-art results in this area, they require large amounts of labeled data, which is costly and time-consuming. Active learning helps optimize this process by selecting the most… Read More »BEAL: A Bayesian Deep Active Learning Method for Efficient Deep Multi-Label Text Classification Sana Hassan Artificial Intelligence Category – MarkTechPost

[[{“value”:” Model efficiency is important in the age of large language and vision models, but they face significant efficiency challenges in real-world deployments. Critical metrics such as training compute requirements, inference latency, and memory footprint impact deployment costs and system responsiveness. These constraints often limit… Read More »Google AI Introduces LAuReL (Learned Augmented Residual Layer): Revolutionizing Neural Networks with Enhanced Residual Connections for Efficient Model Performance Sajjad Ansari Artificial Intelligence Category – MarkTechPost