講者

頁數: 1 2 3 4 - 每頁 20 筆

共有 63 位講者

人工智慧演講:

高宏宇

高宏宇 Deeper Text Mining

本演講將針對深度學習在文字探勘問題上的應用。從命名單元的辨識與擷取中條件亂數場域(conditional random field, CRF)的應用,對話生成模型的差異,到網路謠言偵測三個議題來討論機器學習與深度學習在文字探勘領域的發揮。
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洪士灝

洪士灝 為 AI 打造系統、用 AI 設計系統

人工智慧(AI)蔚為熱潮,應用面不斷地擴張,然而AI要真正落實於產業,還是必須仰賴優質的系統軟硬體統合設計,以及高度優化的系統晶片,才能孕育出有高度競爭力,乃至於造就破壞性創新的AI產品與服務。如今用GPU加速機器學習,已是兵家常事,Google早在2014開始研發高效低耗能的Tensorflow處理器,如今更是百家爭鳴,不僅為AI設計系統,更以AI輔助設計系統。然而這些並非傳統的硬體設計,要能夠集結各種人才,針對應用的特性,整合與創新軟硬體,才能引領潮流。
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洪瑞鴻

洪瑞鴻 淺談深度計算基因體學

Thanks to the advance of many ground breaking technologies in sequencing, the scope of Genomics has expanded several times in recent years. These extremely high throughput technologies generate information in an unprecedented rate; however, leveraging these data to facilitate the understanding to the messages encoded in DNA is still challenging. Although with a complete three-billion-base human genome sequence in hand, we human embarrassingly know only next to nothing to the basis of Genomics. Just before all of us are submerged by the tsunami of data, deep learning, the once glimmering machine learning discipline, has resurrected and bailed us out. With the help of GPU computing and Data Science, we can now let machines discover the mechanisms underlying biological phenomena and pathways with only little domain knowledge. Welcome to the era of Deep Computational Genomics!
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洪智傑

洪智傑 智慧城市的前瞻基礎建設:軌跡資料分析系統

為了建造一個智慧城市,資料分析系統可以稱為是其中的前瞻基礎建設,是不可或缺的一環。其中,軌跡資料是其中來源最多也最具有廣泛應用價值的資料。由於軌跡資料包含使用者位置的資料。這些軌跡資料可以反映使用者如何在城市中活動、如何利用公共運輸設施、如何與城市中的各個角色互動等,因此可以用來讓智慧城市的各項服務品質更好、更有智慧。在此演講中,我們將分享關於軌跡資料系統處理的研究及經驗。其中包含了大規模的即時軌跡資料建模、批次軌跡資料簡化技巧、以及軌跡資料豐富化及應用等,以及針對在GPS軌跡以及捷運卡的資料集上的應用進行案例探討。此演講也希望聽眾在此演講之後能夠對軌跡資料分析於智慧城市的應用有更深一層認識。
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孫民

孫民 Training a Deep Agent to See and Interact

An intelligent agent should have the abilities to see and interact with the world in many different ways. In this talk, I summarize our recent work on seeing and interacting using language (e.g., video captioning), interacting by taking actions in specific applications (e.g., viewing angle selection in 360 videos), and interacting by attacking other agents. Ultimately, we long for an embodied intelligent agent to assist us in our daily life. Hence, we also propose a system to anticipate human intention in order to proactively provide assistance.
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許之凡

許之凡 當人臉遇到機器學習

凱吉哥真的是凱吉哥?人臉偵測與辨識從古至今一直是電腦視覺領域中非常熱門的題目,而解析人臉的第一步即為找出各部件 (眼睛、鼻子、嘴巴) 的位置,再更進一步解析其外型,利用數個關鍵點來代表各種不同的外型,這些關鍵點無論在哪個臉部部件中一般被統稱為"臉部特徵點" (facial landmarks)。臉部特徵點的應用繁多,從頭部姿勢辨識、虛擬化妝、至臉部外型交換,今年 iPhone X 甚至利用 FaceID 臉部辨識技術來進行安全認證,解除手持式裝置的安全鎖。講者將在本演講中,介紹近幾年如何利用機器學習與深度學習的方式在影像中尋找臉部特徵點的位置,進而介紹如何利用機器學習模型來改變人臉的外型。
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張佳彥

張佳彥 Machine Learning in Cyber Security

Machine Learning is a powerful tool to detect the unknown cyber threat. We are going to share our experience of using machine learning in Cyber Security.
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張智威 (Edward Y. Chang)

張智威 (Edward Y. Chang) Representation Learning on Big and Small Data

The approaches in feature extraction can be divided into two categories: model-centric and data-driven. The model-centric approach relies on human heuristics to develop a computer model to extract features from data. These models were engineered by scientists and then validated via empirical studies. A major shortcoming of the model-centric approach is that unusual circumstances that a model does not take into consideration during its design can render the engineered features less effective. Contrast to the model-centric approach, which dictates representations independent of data, the data-driven approach learns representations from data. Example data-driven algorithms are multilayer perceptron (MLP) and convolutional neural network (CNN), which belong to the general category of neural network and deep learning. In this talk I will first explain why my team at Google in 2006 embarked on the data-drive approach. In 2008, we funded the ImageNet project at Stanford, and subsequently in 2011 filed two data-driven patents, one on data-driven feature extraction and the other on data-driven object recognition. We parallelized five widely used machine learning algorithms including SVMs, PFP, LDA, Spectral Clustering, and CNN, and open-sourced all these algorithms. Recently at HTC, we announced DeepQ Open AI platform, which features these scalable algorithms with enhancements. In particular, I will explain how we have made configuring a distributed system to run CNN both simple and cost effective. In 2012, the world was convinced that the data-driven approach to be effective, after Alexnet achieved breakthrough accuracy in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition. This talk will walk through subsequent enhancements on Alexnet in three perspectives: representation ability, optimization ability, and generalization ability. Unfortunately, most applications face the problem of small data. I will share our experiences with transfer learning and GANs, both positive and negative ones. This talk concludes with a list of open research issues.
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張宴晟

張宴晟 Real Time Human Body Segmentation on Mobile Device with Deep Learning

Image Segmentation is quite useful for image understanding, however, the computation is considerable as well. In this talk, I'm going to introduce how we utilize deep learning to segment human body, what problems we met and how we resolve it.
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張家齊

張家齊 Quantitative Trading from Zero to One: From Rule-based to Artificial Intelligence

本演講將介紹如何從無到有的構建與設計出一組交易策略。如何從最基本的 Rule-based 的策略,建構出擁有 Machine Learning 或 AI 模式的交易策略與模型。一般來說,交易策略的建構大致上可以分成 Botton-Up 和 Top-Down 兩種,在本次的演講中,我們也將介紹兩種建構模式的差異,以及他們彼此相輔相成的使用方式。希望,能讓大家對自己手上的策略能更了解,也能更知道如何開始下手改善自己手上的策略!
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張智傑

張智傑 理財?機器人?理財機器人?

理財不再是有錢人的特權!過去理財一直都是高端資產客戶能享有的服務,僅有少部分的理專會提供所謂"mass"客戶的理財規劃。在大數據及資料科學的發展下,為了服務更多的"mass"客戶,基於演算法的理財服務漸漸浮出水面,透過了解客戶屬性、投資風險及目標規劃出個人化的投資組合,並在較低投資門檻下完成理財服務。未來投資及理財不再是夢想。
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張智星

張智星 音樂檢索與歌聲抽取

本演講將回顧音樂檢索的過去與現況,特別是在哼唱選歌及音訊指紋辨識這兩個領域,並說明目前音樂檢索所碰到的最大挑戰。針對這個挑戰,我們將解釋如何從複音音樂進行歌聲抽取以及其重要性。在不同的應用情境下,我們使用的方法包含深度神經網路以及主動是噪音消除,同時我們也將說明如何將抽取出來的歌聲用於各項音樂相關的應用,包含哼唱選歌、口水歌辨識、歌詞對位、歌聲評分等,現場並會進行各項相關展示。
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曹昱

曹昱 Machine Learning and Signal Processing for Assistive Hearing and Speaking Devices

With the rapid advancement in speech processing technologies and in-depth understanding of human speech perception mechanism, significant improvement has been made in the design of assistive hearing devices [assistive listening device (ALD), hearing aids (HAs), and cochlear implants (CIs)] to benefit the speech communication for millions of hearing-impaired patients and subsequently enhance their quality of life. However, there are still many technical challenges, such as designing noise-suppression algorithms catered for ALD, HA, and CI users, deriving optimal compression strategies, improving the music appreciation, optimizing speech processing strategies for users speaking tonal languages, to name a few. In this talk, we present our recent research achievements using machine learning and signal processing on improving speech perception abilities for ALD, HA, and CI users.
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黃士傑

黃士傑 AlphaGo-深度學習與強化學習的勝利

本次演講我將談到AlphaGo發展歷程中的幾個關鍵(特別是深度學習與強化學習所帶進的重大突破),2017年初Master網路60局的小故事,以及在中國烏鎮AlphaGo與柯潔九段的人機大戰。
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黃維中

黃維中 預測式分析於商務服務之應用與挑戰

預測式分析並非新的技術, 過去存在許多統計與經濟模型來預測各種實體跟虛擬世界的行為跟現象。隨著目前資料科技與人工智慧技術的發展, 如何結合龐大的資料來源, 能更有效地進行預測分析, 成為一個有趣且具挑戰的議題。本演講將從整體架構跟實務舉例的方式來跟各愛好者共同探討預測式分析的各種樣貌與未來發展潛力。
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黃從仁

黃從仁 認知神經科學 x人工智慧

認知神經科學和人工智慧兩個領域息息相關。認知神經科學研究人類腦神經網路如何賦予我們做物體辨識、語言理解、計畫與決策等認知能力,而人工智慧則透過使用各種類神經網路與演算法來達成如人般的認知歷程。如原本是認知神經科學家的DeepMind執行長Demis Hassabis博士所言:認知神經科學能對人工智慧能有突破性的啟發。反之,人工智慧也對理解人類大腦如何運作有革命性的影響。本場演講將藉由一些實例討論這兩個領域如何互利互用,並鼓勵雙方能有更緊密的交流與互動。
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黃鐘揚

黃鐘揚 Bottender: An Open Source Bot Framework for Multi-Platform

如果說 bot 是人類實現 AI 應用最直接也是最生活化的管道,那麼 bot framework 就是讓這些 AI 應用得以實現的最重要武器。 Bot (aka Chatbot) 在社群網路、行動裝置全面走進人類生活的今日扮演著「產業智能化」的一個關鍵角色 — 透過自然語意的理解,它成為產業及其用戶溝通的智能客服;在收集用戶的對話記錄、瀏覽行為之後,它得以在對話中推薦最適合的商品;串接了 speech, image/video, 以及各種人機介面的 cognitive services 之後,它的應用甚至可以延伸到如智慧家庭、商辦、工廠、醫院… 等場域。 Bottender 是我們提出、建造的一個開源 bot 建置平台,它提供了模組化的開發環境讓開發者得以快速地串接各種群通訊平台以及網路/雲端架構,而極富彈性的事件處理 APIs 讓複雜的 bot 邏輯得以輕易地實現,並且串接各式各樣的 AI 技術,至於它的各種 session store 機制則讓 bot 可以在各種情境下進行優化,最後,它也提供了整合測試的環境讓系統情境測試自動化得以實現。
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邱中鎮

邱中鎮 Recent advances of deep learning in Google

這場演講將分享Google在深度學習和TensorFlow上近期的發展與應用。分享的內容分三個主題: 深度學習的研究、深度學習的應用、以及TensorFlow。在深度學習的研究上會分享包括learning to learn, sequence-to-sequence, transformer, reinforcement learning等內容;在深度學習的應用上會分享包括醫療保健, 機器人, 科學計算, 藝術, 以及Google相關產品等內容;在TensorFlow方面將分享平台上的新發展以及TPU等
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頁數: 1 2 3 4 - 每頁 20 筆

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