Talks

למידה עמוקה (deep learning)  הוא תחום שישנה את חיינו – תיקון – הוא כבר משנה את חיינו. בהרצאה זו שניתנה לסטודנטים בטכניון ולמתעניינים אחרים אני מספר את סיפורו המרתק של התחום הזה ואת התהפוכות שהוא עבר בששים השנים האחרונות. הרצאה זו נועדה לקהל הרחב ולא דורשת כל ידע מוקדם.

קישור זה מפנה לסרט וידאו של הרצאה זו שהוקלט באולפן.

קישור זה מפנה לסרט וידאו של הקלטת מפגש הזום (איכות האודיו נומוכה יותר) בו העברתי את אותה הרצאה.

למידה עמוקה (deep learning)  הוא תחום שישנה את חיינו – תיקון – הוא כבר משנה את חיינו. בהרצאה זו שניתנה לגורמי צה”ל, אני מספר את סיפורו המרתק של התחום הזה ואת התהפוכות שהוא עבר בששים השנים האחרונות. הרצאה זו נועדה לקהל הרחב ולא דורשת כל ידע מוקדם. זוהי גירסה ארוכה יותר של הרצאה דומה שניתנה ב- 2019 במסיבת היובל לפקולטה למדעי המחשב.

Image Processing is a fascinating scientific field, offering ways to handle visual data by computers. How can an image be brought to be stored and processed by a computer? What kind of such processing could be done which are worthwhile? In the first part of this talk we shall describe the core ideas behind the field of image processing by answering these two questions. In the second part of the talk we shall turn to describe the recent research activity in Elad’s group in the Computer-Science department at the Technion, emphasizing the vast work done on harnessing sparse and redundant representation modeling to image processing needs.

In this survey talk I will walk you through a decade of fascinating research activity on “sparse and redundant representations”. We will start with a classic image processing task of noise removal and use it as a platform for the introduction of data models in general, and sparsity and redundancy as specific forces in such models. The emerging model will be shown to lead to a series of key theoretical and numerical questions, which we will handle next. A key problem with the use of sparse and redundant representation modeling is the need for a sparsifying dictionary – we will discuss ways to obtain such a dictionary by learning from examples, and introduce the K-SVD algorithm. Then we will show how all these merge into a coherent theory that can be deployed successfully to various image processing applications.

Images, video, audio, text documents, financial data, medical information, traffic info – all these and many others are data sources that can be effectively processed. Why? Is it obvious? In this talk we will start by discussing “modeling” of data as a way to enable their actual processing, putting emphasis on sparsity-based models. We will turn our attention to graph-structured data and propose a tailored sparsifying transform for its dimensionality reduction and subsequent processing. We shall conclude by showing how this new transform becomes relevant and powerful in revisiting … classical image processing tasks..

Images, video, audio, text documents, financial data, medical information, traffic info — all these and many others are data sources that can be effectively processed. Why? Is it obvious? In this talk we will start by discussing “modeling” of data as a way to enable their actual processing, putting emphasis on sparsity-based models. We will turn our attention to graph-structured data and propose a tailored sparsifying transform for its dimensionality reduction and subsequent processing. We shall conclude by showing how this new transform becomes relevant and powerful in revisiting … classical image processing tasks.

In this survey talk I will walk you through a decade of fascinating research activity on “sparse and redundant representations”. We will start with a classic image processing task of noise removal and use it as a platform for the introduction of data models in general, and sparsity and redundancy as specific forces in such models. The emerging model will be shown to lead to a series of key theoretical and numerical questions, which we will handle next. A key problem with the use of sparse and redundant representation modeling is the need for a sparsifying dictionary — we will discuss ways to obtain such a dictionary by learning from examples, and introduce the K-SVD algorithm. Then we will show how all these merge into a coherent theory that can be deployed successfully to various image processing applications.

In this survey talk I will walk you through a decade of fascinating research activity on “sparse and redundant representations”. We will start with a classic image processing task of noise removal and use it as a platform for the introduction of data models in general, and sparsity and redundancy as specific forces in such models. The emerging model will be shown to lead to a series of key theoretical and numerical questions, which we will handle next. A key problem with the use of sparse and redundant representation modeling is the need for a sparsifying dictionary — we will discuss ways to obtain such a dictionary by learning from examples, and introduce the K-SVD algorithm. Then we will show how all these merge into a coherent theory that can be deployed successfully to various image processing applications.