The quest to strengthen national security has faced several challenges over the years, especially as the pace of technological advancement has far outstripped the speed of legislative and bureaucratic adaptation. With a growing dependence on technology, the need to protect sensitive information and secure communication channels is more pressing than ever. The complexity of cyber… →
Automatic differentiation has transformed the development of machine learning models by eliminating complex, application-dependent gradient derivations. This transformation helps to calculate Jacobian-vector and vector-Jacobian products without creating the full Jacobian matrix, which is crucial for tuning scientific and probabilistic machine learning models. Otherwise, it would require a column for each neural network parameter. Nowadays, everyone… →
In the rapidly evolving field of artificial intelligence, the focus often lies on large, complex models requiring immense computational resources. However, many practical use cases call for smaller, more efficient models. Not everyone has access to high-end GPUs or vast server infrastructures, and numerous scenarios benefit more from smaller, accessible models. Despite advancements, the complexity… →
Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. Existing methods face challenges in achieving detailed 3D tracking because they often track only a few points, which need more detail for full-scene understanding. They also demand computational power, making it difficult to handle long videos efficiently.… →
Understanding the different forms and future directions of Artificial Intelligence (AI) is becoming increasingly important as it evolves. Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI) are the three primary categories of AI. Each kind marks unique turning points in the development of AI and reflects a varying degree of… →
A fundamental challenge in studying EEG-to-Text models is ensuring that the models learn from EEG inputs and not just memorize text patterns. In many reports in the literature where great results have been obtained on brain signal translation to text, there seems to be reliance on implicit teacher-forcing evaluation methods that could artificially inflate performance… →
Information overload presents significant challenges in extracting insights from documents containing both text and visuals, such as charts, graphs, and images. Despite advancements in language models, analyzing these multimodal documents remains difficult. Conventional AI models are limited to interpreting plain text, often struggling to process complex visual elements embedded in documents, which hinders effective document… →
Current Text-to-Speech (TTS) systems, such as VALL-E and Fastspeech, face persistent challenges related to processing complex linguistic features, managing polyphonic expressions, and producing natural-sounding multilingual speech. These limitations become particularly evident when dealing with context-dependent polyphonic words and cross-lingual synthesis. Traditional TTS approaches, which rely on grapheme-to-phoneme (G2P) conversion, often struggle to manage phonetic complexity… →
Recognition of human motion using time series from mobile and wearable devices is commonly used as key context information for various applications, from health condition monitoring to sports activity analysis to user habit studies. However, collecting large-scale motion time series data remains challenging due to security or privacy concerns. In the motion time series domain,… →
Large language models (LLMs) have become the backbone of many AI systems, contributing significantly to advancements in natural language processing (NLP), computer vision, and even scientific research. However, these models come with their own set of challenges. As the demand for better AI capabilities increases, so does the need for more sophisticated and larger models.… →