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MIT 6.S192 - Lecture 19: Easy 3D content creation with consistent neural fields, Ajay Jain
MIT 6.S192 - Lecture 19: Easy 3D content creation with consistent neural fields, Ajay Jain
In this lecture, Ajay Jain presents his work on neural scene representations, specifically focusing on the Neural Radiance Fields model which uses sparsely sampled input views to construct a representation of a scene's 3D geometry and color. Jain discusses the challenges of fitting a Neural Radiance Field to a single scene, as well as ways to improve the data efficiency of the training process by adding photometric loss and semantic consistency loss. He also talks about using CLIP to remove artifacts in NeRF and generate 3D objects from captions in the project Dream Fields. Other topics include creating consistent foreground objects in scenes, acquiring captioned 3D object datasets, reducing rendering costs, and optimizing the system's performance.
MIT 6.S192 - Lecture 20: Generative art using diffusion, Prafulla Dhariwal
MIT 6.S192 - Lecture 20: Generative art using diffusion, Prafulla Dhariwal
In this lecture, Prafulla Dhariwal from OpenAI discusses the progress of generative modeling for hard creative tasks, particularly with diffusion models. The process involves starting with an image and slowly adding Gaussian noise to it, then reversing the process by taking some noised damage and de-noising it to create less noisy images. The generative model is obtained by training a model to reverse noise like this, producing an image from pure noise at test time by running the model step-by-step backwards. The reverse prediction of the process also looks like a Gaussian distribution when the amount of noise added is very small, which is used to predict the mean and variance of the model. Dhariwal also discusses how to use diffusion models for in-painting and addressing the potential dangers of AI-generated content.
MIT 6.S192 - Lecture 21: Between Art, Mind, & Machines, Sarah Schwettmann
MIT 6.S192 - Lecture 21: Between Art, Mind, & Machines, Sarah Schwettmann
In this lecture, Sarah Schwettmann discusses the intersection between art, mind, and machines. She delves into visual perception and the challenge of experiencing a rich 3D world through a 2D canvas, which requires the brain to solve an inverse problem and construct a best explanation of the incoming information. Schwettmann also talks about projects involving deep generative models trained on artworks, such as the use of GAN inversion to embed Met collection images into a foundation model's feature space to understand the structure of human creativity, and the creation of a visual concept vocabulary for an arbitrary GAN latent space by sampling the space of salient or possible transformations and using those sample directions as a screen to project human perceptual judgments. Human interaction and labeling are important in this process, and the resulting vocabulary can be applied to other models and used to manipulate images in various ways. Despite noise in the data due to varying word choice, their method of distilling vocabularies using any size of annotation library can be scaled up and may involve training a captioner to label directions automatically.
Sarah Schwettmann also discusses various ways to explore and assign meaning to directions within models trained on human creation. She presents an experiment capturing and learning visual directions without language, which allows humans to define the transformation they want purely visually by interacting with a small batch of images sampled from latent space or feature space. This method is useful for labeling and understanding images with nuanced, hard-to-explain features. Moreover, latent space can become a screen onto which human experiences can be projected, allowing researchers to better understand aspects of human perception that are otherwise difficult to formalize.
MIT 6.S192 - Lecture 22: Diffusion Probabilistic Models, Jascha Sohl-Dickstein
MIT 6.S192 - Lecture 22: Diffusion Probabilistic Models, Jascha Sohl-Dickstein
In this lecture, Jascha Sohl-Dickstein discusses diffusion models, which are used to learn tasks that are separate from the training data. The models are probabilistic and can be used to encode or decode data. The forward diffusion process is a fixed process, and the reverse process is also true.
This lecture discusses diffusion probabilistic models and explains that, while there is a one-to-one correspondence between the latent space and the image space, it is possible to work with multiple classes within the same model. The lecture then goes on to explain how to use these models to generate new images.
GenRep: Generative Models as a Data Source for Multiview Representation Learning in ICLR2022
Code: https://github.com/ali-design/GenRep
GenRep: Generative Models as a Data Source for Multiview Representation Learning in ICLR2022
The presenters discuss the concept of model zoos, where pre-trained generative models are made accessible without access to the underlying data. By utilizing contrastive learning, researchers can create different views of the same object, which will fall into the same neighborhood within the representation space. They found that simple gaussian transformations in the latent space were effective and that generating more samples from IGMs leads to better representations. Expert IGMs, such as StyleGAN Car in specific domains, can outperform representations learned from real data. The project website and Github code are available for further exploration.
An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
Gilbert Strang, a renowned mathematician, emphasizes the importance of projects over exams in teaching deep learning, a crucial part of machine learning that heavily relies on linear algebra. He believes that projects allow students to understand how to apply deep learning in the real world and are a more effective way of learning. Strang also emphasizes that teaching is about learning and working with the students rather than solely grading them. He advises new professors to use large chalk and to take their time to stay with the class in order to be successful in teaching.
MIT 18.065. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
Course Introduction of by Professor Strang
Professor Strang introduces his new course 18.065, which covers four key topics: linear algebra, deep learning, optimization, and statistics. The course will focus on the best matrices, symmetric and orthogonal matrices and their relation to linear algebra. It will also cover deep learning, which is foundational to linear algebra and involves complex calculations that can require the use of GPUs over days or even weeks. The course will touch on statistics, which plays a role in keeping the numbers in the learning function within a good range, and optimization and probability theory which, are important in learning algorithms, and differential equations which play a key role in science and engineering applications. The course includes exercises, problems, and discussions to provide a complete presentation of the subject matter.
Lecture 1: The Column Space of A Contains All Vectors Ax
Lecture 1: The Column Space of A Contains All Vectors Ax
This lecture focuses on the concept of the column space of a matrix, which is a collection of all the vectors that can be obtained by multiplying the matrix with all possible vectors. The lecturer explains that the column space depends on the matrix and could be the whole space of R3 or a smaller subset of it. The professor further discusses the concepts of row space, column rank, and row rank, as well as the relationship between these ranks. The lecture also briefly touches upon the first great theorem in linear algebra, which states that the column rank of a matrix equals the row rank of the matrix. Additionally, the professor discusses methods for matrix multiplication and the number of multiplications required for the process. Overall, the lecture offers an introduction to linear algebra and its importance in learning from data.
Lecture 2: Multiplying and Factoring Matrices
Lecture 2: Multiplying and Factoring Matrices
This lecture covers the basics of multiplying and factoring matrices. The author explains how matrices have dimensions in both the row and column spaces, and how the row space has dimension R while the null space has dimension M minus R. The lecture also discusses the relationship between rows and solutions to an equation, as well as the orthogonality of vectors in two-dimensional space. Finally, the author explains the fundamental theorem of linear algebra, which states that the dimensions of a space come out right when the geometry is worked out.
Lecture 3. Orthonormal Columns in Q Give Q'Q = I
3. Orthonormal Columns in Q Give Q'Q = I
This section of the video explains the concept of orthogonal matrices and their significance in numerical linear algebra. The speaker proves that the length squared of QX must be the same as X transpose QX using the fact that Q transpose Q equals the identity. The video also discusses constructing orthogonal matrices using various methods such as Gordan matrices and Householder matrices. The importance and construction of wavelets is also explained, along with the concept of using orthogonal eigenvectors in signal processing. Finally, the speaker talks about how to test orthogonal vectors with complex numbers and mentions that orthogonal matrices have orthogonal eigenvectors with different eigenvalues.