Deep Multi-task Representation Learning on Tabular Data

Presents: Jacobo G. González León

7th PDTA

Thesis advisors:

  • PhD. Miguel Félix Mata Rivera
  • PhD. Rolando Menchaca Méndez

(Mixed) Tabular Data

\(X\) : features

\(y^T\) : target

4 categorical, 30 numerical

2 categorical

Correlation

Preprocessed Data (VSM)

\(X\) : features

296 numerical (from categorical), 30 numerical

Long-tail distribution

Correlation

Preprocessed Data (VSM)

\(y\) : targets

Isolated Learning

Autoencoder

\(f(X)\)

\(g(h)\)

\( f(\hat{y}_i) = \frac{e^{\hat{y}_i}}{\sum_{i}^C {e^{\hat{y}_i}}} \)

\( \mathcal{L}= {\sum_{i}^C {e^{\hat{y}_i}}}{\log{f(\hat{y}_i)}} \)

Multi-class Classifier

Loss function

Loss function

softmax

cross entropy loss

\( \mathcal{L}=  \text{\textbardbl}{X-\hat{X}}\text{\textbardbl} \)

reconstruction error

TASK 0

TASK 1

TASK 2

1)

2)

Research question

Can we design a centralized architecture that learns from multi-task simultaneously ?

Design a low-dimensional multitasking representation for mixed preprocessed data

Problem definition

Loss function

weighted cross entropy loss

Multi-task Learning

TASK 1

TASK 2

\( \mathcal{L}=  {\psi_1} {\sum_{i}^C {e^{\hat{y}_i^1}}}{\log{f(\hat{{y}_i^1})}} + {\psi_2} {\sum_{i}^C {e^{\hat{y}_i^2}}}{\log{f(\hat{{y}_i^2})}} \)

TASK 1 + TASK 2

\({\psi_1}\)

\({\psi_2}\)

Deep Multi-task Learning

ENCODER + TASK 1 + TASK 2

Loss function

weighted cross entropy loss

\( \mathcal{L}= {\psi_1} {\sum_{i}^C {e^{\hat{y}_i^1}}}{\log{f(\hat{{y}_i^1})}} + {\psi_2} {\sum_{i}^C {e^{\hat{y}_i^2}}}{\log{f(\hat{{y}_i^2})}} \)

\({\psi_1}\)

\({\psi_2}\)

Deep Multi-task Representation Learning

TASK 0 + TASK 1 + TASK 2

Loss function

weighted reconstruction error with weighted cross entropy loss

\( \mathcal{L}= {\psi_0} \text{\textbardbl}{X-\hat{X}}\text{\textbardbl} + {\psi_1} {\sum_{i}^C {e^{\hat{y}_i^1}}}{\log{f(\hat{{y}_i^1})}} + {\psi_2} {\sum_{i}^C {e^{\hat{y}_i^2}}}{\log{f(\hat{{y}_i^2})}} \)

\({\psi_1}\)

\({\psi_2}\)

\({\psi_0}\)

Validation (Methodology)

80 %

10 %

10 %

40 %

40 %

20 %

20 %

10 %

10 %

Warm-up

Trainning

Test/Val

60 %

20 %

Targets

Model Validation Results

SIMILARITIES

TASK 1

TASK 2

Model similarities

Train/Val/Test Results

Warm-up

Trainning

Loss function

Warm-up

Trainning

Accuracy TASK 1

Train/Val/Test Results

Warm-up

Trainning

Accuracy TASK 2

Train/Val/Test Results

Representation Results

ISOLATED

T0 \(\psi_0=1\)

\(R2:0.99\)

T1 \(\psi_1=1\)

\(ACC:0.9997\)

T2 \(\psi_2=1\)

\(ACC:0.9998\)

DMTL

F \(\psi_1=1 , \psi_2=1\)

E \(\psi_1=0.25, \psi_2=0.25\)

mE \(\psi_1=1E-5, \psi_2=1E-5\) 

Representation Results

DMTRL

T0 \(\psi_0=1\)

\(R2:0.986\)

E \(\psi_0=0.1, \psi_1=0.1 , \psi_2=0.1\)

\(R2:0.66\)

F \(\psi_0=1, \psi_1=0.1 , \psi_2=0.1\)

\(R2:0.79\)

Representation Results

DMTRL

mE2 \(\psi_0=0.1, \psi_1=1E-5 , \psi_2=0.0001\)

\(R2:0.90\)

mE3 \(\psi_0=0.01, \psi_1=0.0001 , \psi_2=1E-5\)

\(R2:0.75\)

mF \(\psi_0=1, \psi_1=1E-5 , \psi_2=1E-5\)

\(R2:0.980\)

Representation Results

Validation Results

CORRELATIONS

Global features

  • 'TOTAL TRAINNING SECONDS',  'BEST TRIAL SECONDS',
  • 'TASK 0 PSI 0', 'TASK 1 PSI 1', 'TASK 2 PSI 2',
  • 'TASK 0 TOTAL PARAMETERS', 'TASK 1 TOTAL PARAMETERS', 'TASK 2 TOTAL PARAMETERS', 'TOTAL TRAINABLE PARAMETERS', 
  • 'BATCH SIZE', 'OPTIMIZER', 'LR',     

Models features

  • 'TASK 0 ACTIVATION', 'TASK 1 ACTIVATION', 'TASK 2 ACTIVATION',

  • 'TASK 0 FC1 ENCODER', 'TASK 0 FC2 ENCODER , 'TASK 0 FC3 ENCODER', 'LATENT SPACE',  'TASK 0  FC1 DECODER', 'TASK 0  FC2 DECODER', 'TASK 0  FC3 DECODER',

  • 'TASK 1 FC1', 'TASK 1 FC2', 'TASK 1 FC3',

  • 'TASK 2 FC1', 'TASK 2 FC2', 'TASK 2

Global features

  • 'TOTAL TRAINNING SECONDS',  'BEST TRIAL SECONDS',
  • 'TASK 0 PSI 0', 'TASK 1 PSI 1', 'TASK 2 PSI 2',
  • 'TASK 0 TOTAL PARAMETERS', 'TASK 1 TOTAL PARAMETERS', 'TASK 2 TOTAL PARAMETERS', 'TOTAL TRAINABLE PARAMETERS', 
  • 'BATCH SIZE', 'OPTIMIZER', 'LR',     

Warm-up features

  • 'TASK 0 TRAIN LOSS WARM-UP', 'TASK 0 VALID LOSS WARM-UP',

  • 'TASK 1 TRAIN ACC WARM-UP', 'TASK 1 VALID ACC WARM-UP', 

  • 'TASK 2 TRAIN ACC WARM-UP', 'TASK 2 VALID ACC WARM-UP', 

Validation Results

CORRELATIONS

Global features

  • 'TOTAL TRAINNING SECONDS',  'BEST TRIAL SECONDS',
  • 'TASK 0 PSI 0', 'TASK 1 PSI 1', 'TASK 2 PSI 2',
  • 'TASK 0 TOTAL PARAMETERS', 'TASK 1 TOTAL PARAMETERS', 'TASK 2 TOTAL PARAMETERS', 'TOTAL TRAINABLE PARAMETERS', 
  • 'BATCH SIZE', 'OPTIMIZER', 'LR',     

Task 0 results features

  • 'TASK 0 XTEST RMSE', 'TASK 0 XTEST MSE', 'TASK 0 XTEST R2',

  • 'TASK 0 XVAL RMSE', 'TASK 0 XVAL MSE', 'TASK 0 XVAL R2',

  • 'TASK 0 XTRAIN RMSE', 'TASK 0 XTRAIN MSE', 'TASK 0 XTRAIN R2',    

Validation Results

CORRELATIONS

Global features

  • 'TASK 0 XTEST RMSE', 'TASK 0 XTEST MSE', 'TASK 0 XTEST R2',

  • 'TASK 0 XVAL RMSE', 'TASK 0 XVAL MSE', 'TASK 0 XVAL R2',

  • 'TASK 0 XTRAIN RMSE', 'TASK 0 XTRAIN MSE', 'TASK 0 XTRAIN R2',

  • 'TASK 1 YTEST ACC', 'TASK 1 YTEST PRCS', 'TASK 1 YTEST RCLL',

  • 'TASK 1 YVAL ACC', 'TASK 1 YVAL PRCS', 'TASK 1 YVAL RCLL',

  • 'TASK 1 YTRAIN ACC', 'TASK 1 YTRAIN PRCS', 'TASK 1 YTRAIN RCLL',

  • 'TASK 2 YTEST ACC', 'TASK 2 YTEST PRCS', 'TASK 2 YTEST RCLL',

  • 'TASK 2 YVAL ACC', 'TASK 2 YVAL PRCS', 'TASK 2 YVAL RCLL',

  • 'TASK 2 YTRAIN ACC', 'TASK 2 YTRAIN PRCS', 'TASK 2 YTRAIN RCLL'

Validation Results

CORRELATIONS

  • Contributions:
    • Models:
      • Isolated, Multi-task, Deep Multi-task, Deep Multi-task Representation Learning approaches
      • Shared weights contributions
    • Methodologies:
    • Data train/test/val split strategy
    • Models warm-up/trainning strategy
    • Metrics & validation strategy
    • Results correlations and similarities strategy

Conclusions

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