---
# System prepended metadata

title: The 3rd Augmented Intelligence and Interaction (AII) Workshop
tags: [workshop, shared]

---

# The 3rd Augmented Intelligence and Interaction (AII) Workshop
###### tags: `shared` `workshop`

[TOC]

## About this workshop
+ This workshop is held by Prof. Min Sun from June 30 to July 1
+ Detailed information can be found in this page: http://aliensunmin.github.io/aii_workshop/3rd/

## Keynote speech
+ K1: On Adversarial Learning
    - Traditional Model Learning
        - Maximum likelihood learning (with regularization): $\theta = argmax \quad \mathbb{E}_q \log p \Rightarrow argmin \quad KL(q\Vert p)$ 
        - Likely that the samples drawn from model distribution will NOT look realistic
        - Model distribution will not have mass where there’s no support of the true distribution
        - How about interchange p (model) & q (true)? (q is not available though)
            - Likely to look realistic, but may only represent (very small) a subset of possible data
            - Use entropy of p as regularizer to let p (model) spread out
            - theta = argmax E_p log (q/p) = argmin KL(p||q)
            - Problem: not knowing q => Is there a method to know q?
    - Implementation details
        - Critic: g(x) = log (q/p)
        - Optimal classifier should be able to discriminate between real/fake samples
    - i.e., GAN
    - Q: What is the role for GAN in classification?
        - GAN is a way to synthesize data
        - Small unlabelled and large labelled learning => semi-supervised learning

+ K2: Object-Preserving Cross-Domain Image Translation for Adaptive Object Detection
    - Domain adaptation in object detection
        - Paired/Un-paired training images; un-paired case is more practical
        - Multimodal image translation 
    - GAN, conditional GAN (to different domain), cycle GAN, AugGAN

## Invited speech

+ Meta Learning of Figure-Ground Segmentation
    - Region-of-interest from user feedback (whether is in the region (yes/no))
        - Feedback Segmentation using Transductive Learning (whether a point is in the region)
        - SwipeCut (whether line is in the region of interest)
        - Tap&Shoot (tapping focus)
    - Learning by Editing (unsupervised learning with GAN): Visual-Effect GAN (VEGAN)

+ On Manageable Visual Storytelling
    - Given photos => text story
        - One story is not multiple image captions
        - Cohesion & coherence, creativeness, visual: grounding
        - E2E models are hardly manageable
    - Preliminary results showed
        - Model does NOT know how to describe things NOT in the training set
        - Need more data (due to small datasets)
        - Divide and conquer: image/scene understanding -> story generation
        - Semantic layer in between these two steps: FrameNet Terms (verbs and nouns)

+ Network Representation Learning and its Applications
    - Network embedding applications: user identification (sharing accounts) (SIGIR)
        - Representing data as a heterogeneous net
            - Nodes: items/meta info
            - Find mappings for nodes to low-dimensional representation
            - User ID as ground truth
        - Hybrid account-user recommender
    - MARINE (WWW)

+ Research at Taiwan AI Labs: Music AI
    - Pipeline: audio in, audio out
        - Source separation -> music transcription -> composition -> synthesis

+ Exploration via Flow-Based Intrinsic Rewards
    - Curiosity-Driven exploration challenges

+ AutoML: Who is Designing Your Neural Net
    - AutoML
        - Human should focus on problem formulation
        - AutoML in industry: Google cloud AutoML
    - Neural Architecture Search (NAS)
        - Automating architecture design
        - Subfield of AutoML
        - RL-based & EA-based approaches
    - NAS: recent trends
        - Multi-objective NAS
        - Distribution of architectures
        - Accelerating NAS
    - What’s the next wave
        - Federated learning + NAS

+ Towards Unsupervised Speech Recognition
    - Why unsupervised learning? 
        - More than 7000 languages
        - Labelling is labour intensive
    - Acoustic token discovery
        - Problem: token is not readable
        - To speech recognition: need a table between tokens and texts
        - Introduction of GAN: find a better and better mapping network
        - Through a totally unsupervised learning
        - Jointly learn token discovery and mapping table
    - Learn from itself
        - Pseudo labels
        - Bootsrapping
    - How about semi-supervised learning?

+ Goal-Driven-Based Speech Enhancement and its Applications to Assistive Hearing Device
    - Speech enhancement
        - Replacing the original norm objective constraints to other specific goals

+ A Semantic Approach to Abstractive Summarization
    - Extractive/Abstractive
    - LCSTS Chinese Text Summarization Datasets
