At Tenyx, we’ve delved into the intricate workings of Large Language Models (LLMs) to uncover the geometric structures underlying their reasoning capabilities. Our research provides new insights into how LLMs process information and the implications for improving their reasoning abilities. Read more
At Tenyx, we’ve spent countless hours peering into the intricate workings of Large Language Models (LLMs). Today, we’re excited to share our research, in collaboration with Brown University, that sheds light on the geometric structures and transformations governing these models. Our work provides new insights into how LLMs process their inputs and the implications for AI safety in applications driven by LLMs. Read more
Fine-tuning is a common procedure by which a pretrained language model is updated with training on a domain-specific dataset to improve performance in that domain (i.e. a chatbot to answer enterprise-specific Q&A, a hotel booking agent). It has been known for some time (if not widely appreciated) that fine-tuning a model on new data degrades its performance on the initial pretraining dataset (the dreaded “catastrophic forgetting” problem in ML). But by how much? And do all fine-tuning methods degrade performance in the same ways, and to the same extent? Read more
When we think about Multi-Layer Perceptrons (MLPs), we often visualize them as interconnected neurons processing information. However, there’s an elegant alternative perspective - viewing MLPs as hashing functions that partition input space and mapping functions on these partitions. Read more
When we think about Multi-Layer Perceptrons (MLPs), we often visualize them as interconnected neurons processing information. However, there’s an elegant alternative perspective - viewing MLPs as hashing functions that partition input space and mapping functions on these partitions. Read more
Standard protocols for benchmarking self-supervised models involve using a linear or k-nearest neighbor classification on frozen features of the learned model. However, both evaluations are sensitive to hyperparameters making the evaluation and comparison complicated. Read more
Generative modeling is a branch of machine learning that attempts at modeling the probability distribution of high dimensional data, for example - images Read more
In this post, we will look at a particular view of uncertainty in modern deep learning systems using droput Read more
Generative modeling is a branch of machine learning that attempts at modeling the probability distribution of high dimensional data, for example - images Read more
Model pruning in neural networks was the answer I ended up with when I got wondering about the workings of dropout, dropconnect and papers like Do Deep Nets Really Need to be Deep? Read more
Neural networks can be viewed as layers of building blocks neurons made up of weights, biases and activation function. A fundamental understanding of how these basic units function could help one in achieving one’s objective Read more
Image processing over the years has evolved from simple linear averaging filters to highly adaptive non linear filtering operations such as the bilateral filter (BF), moving least squares, BM3D and LARK to name a few. Read more
Graph driven machine learning has seen a surge of interest in the past few years with several applications in social sciences, biology, and network analysis, to name a few. However, in some scenarios, no graph is given a priori and one one has to infer and construct a graph to fit the data given. Read more
Image processing over the years has evolved from simple linear averaging filters to highly adaptive non linear filtering operations such as the bilateral filter (BF), moving least squares, BM3D and LARK to name a few. Read more
Standard protocols for benchmarking self-supervised models involve using a linear or k-nearest neighbor classification on frozen features of the learned model. However, both evaluations are sensitive to hyperparameters making the evaluation and comparison complicated. Read more
Generative modeling is a branch of machine learning that attempts at modeling the probability distribution of high dimensional data, for example - images Read more
In this post, we will look at a particular view of uncertainty in modern deep learning systems using droput Read more
Generative modeling is a branch of machine learning that attempts at modeling the probability distribution of high dimensional data, for example - images Read more
Model pruning in neural networks was the answer I ended up with when I got wondering about the workings of dropout, dropconnect and papers like Do Deep Nets Really Need to be Deep? Read more
Neural networks can be viewed as layers of building blocks neurons made up of weights, biases and activation function. A fundamental understanding of how these basic units function could help one in achieving one’s objective Read more
At Tenyx, we’ve delved into the intricate workings of Large Language Models (LLMs) to uncover the geometric structures underlying their reasoning capabilities. Our research provides new insights into how LLMs process information and the implications for improving their reasoning abilities. Read more
Graph driven machine learning has seen a surge of interest in the past few years with several applications in social sciences, biology, and network analysis, to name a few. However, in some scenarios, no graph is given a priori and one one has to infer and construct a graph to fit the data given. Read more
At Tenyx, we’ve spent countless hours peering into the intricate workings of Large Language Models (LLMs). Today, we’re excited to share our research, in collaboration with Brown University, that sheds light on the geometric structures and transformations governing these models. Our work provides new insights into how LLMs process their inputs and the implications for AI safety in applications driven by LLMs. Read more
Fine-tuning is a common procedure by which a pretrained language model is updated with training on a domain-specific dataset to improve performance in that domain (i.e. a chatbot to answer enterprise-specific Q&A, a hotel booking agent). It has been known for some time (if not widely appreciated) that fine-tuning a model on new data degrades its performance on the initial pretraining dataset (the dreaded “catastrophic forgetting” problem in ML). But by how much? And do all fine-tuning methods degrade performance in the same ways, and to the same extent? Read more