Regression tasks, which involve predicting continuous numeric values, have traditionally relied on numeric heads such as Gaussian parameterizations or pointwise tensor projections. These traditional ...

Mathematical reasoning remains one of the most complex challenges in AI. While AI has advanced in NLP and pattern recognition, its ability to solve complex mathematical problems with human-like logic ...

As the need for high-quality training data grows, synthetic data generation has become essential for improving LLM performance. Instruction-tuned models are commonly used for this task, but they often ...

Large foundation models have demonstrated remarkable potential in biomedical applications, offering promising results on various benchmarks and enabling rapid adaptation to downstream tasks with ...

Time series forecasting presents a fundamental challenge due to its intrinsic non-determinism, making it difficult to predict future values accurately. Traditional methods generally employ point ...

In this tutorial, we demonstrate how to efficiently fine-tune the Llama-2 7B Chat model for Python code generation using advanced techniques such as QLoRA, gradient checkpointing, and supervised ...

Real-time speech translation presents a complex challenge, requiring seamless integration of speech recognition, machine translation, and text-to-speech synthesis. Traditional cascaded approaches ...

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly ...

Code generation models have made remarkable progress through increased computational power and improved training data quality. State-of-the-art models like Code-Llama, Qwen2.5-Coder, and ...

Efficient long-context inference with LLMs requires managing substantial GPU memory due to the high storage demands of key-value (KV) caching. Traditional KV cache compression techniques reduce memory ...

As deep learning models continue to grow, the quantization of machine learning models becomes essential, and the need for effective compression techniques has become increasingly relevant. Low-bit ...

Databases are essential for storing and retrieving structured data supporting business intelligence, research, and enterprise applications. Querying databases typically requires SQL, which varies ...