# ExioML: Eco-economic dataset for Machine Learning in Global Sectoral
Sustainability

Yanming Guo
School of Electrical & Computer Engineering
University of Sydney
<EMAIL_ADDRESS>
&Jin Ma
School of Electrical & Computer Engineering
University of Sydney
<EMAIL_ADDRESS>

###### Abstract

The Environmental Extended Multi-Regional Input-Output analysis is the
predominant framework in Ecological Economics for assessing the environmental
impact of economic activities. This paper introduces ExioML, the first Machine
Learning benchmark dataset designed for sustainability analysis, aimed at
lowering barriers and fostering collaboration between Machine Learning and
Ecological Economics research. A crucial greenhouse gas emission regression
task was conducted to evaluate sectoral sustainability and demonstrate the
usability of the dataset. We compared the performance of traditional shallow
models with deep learning models, utilizing a diverse Factor Accounting table
and incorporating various categorical and numerical features. Our findings
reveal that ExioML, with its high usability, enables deep and ensemble models
to achieve low mean square errors, establishing a baseline for future Machine
Learning research. Through ExioML, we aim to build a foundational dataset
supporting various Machine Learning applications and promote climate actions
and sustainable investment decisions. The codes are available:
https://github.com/Yvnminc/ExioML

##  1 Introduction

The increase in Greenhouse Gas (GHG) emissions due to fossil-fuel-driven
economic development has precipitated a global warming crisis. This concern
led to establishing the Paris Agreement in 2015, aiming to limit long-term
temperature rise to no more than 2 ∘C above pre-industrial levels (Schleussner
et al., 2016). Concurrently, the Sustainable Development Goals (SDGs),
specifically Goal 13 and Goal 8, were proposed to emphasize taking climate
action to reduce emissions while maintaining economic growth. To address such
climate-trade dilemma, researchers from various disciplines strive to balance
climate action with economic growth and human well-being (Rao et al., 2014;
Jorgenson, 2014). Recently, the Machine Learning (ML) technique (LeCun et al.,
2015) has emerged as a significant tool for accurate prediction to assist
climate change decision-making (Rolnick et al., 2022). Specifically, ML
algorithms have been explored in aiding nearly real-time global weather
forecasting (Lam et al., 2023), land monitoring via satellite imagery
(Stanimirova et al., 2023; He et al., 2016), and the prediction of
disturbances in electric grids (Zheng et al., 2022).

However, unlike the open-source culture of the ML community (Zheng et al.,
2022), most Ecological Economics (EE) studies haven’t publicised the code and
data. This leads to fragmented, independent, and inconsistent research
leveraging ML techniques to explore the sustainability of different sectors
and regions. Subsequent research cannot fairly compare the model performance,
such as accuracy, robustness and generalisation or reproduce the result based
on previous research, leading to the slow EE-ML development (Zhu et al., 2023;
Ballarin et al., 2023; Nangini et al., 2019). We proposed ExioML as the first
ML-ready benchmark data in EE research. The ExioML is developed on top of the
high-quality open-source EE-MRIO dataset ExioBase 3.8.2 with high
spatiotemporal resolution (Stadler et al., 2018). Enabling tracking resource
flow between international sectors for global sectoral sustainability
assessment. The usability of the ExioML dataset was validated using
multimodality regression to quantify the sectoral GHG emission, achieving a
low MSE result. This establishes a critical baseline for accuracy and
efficiency in future ML research.

##  2 Literature Review

No public benchmark Ecological Economics and Machine Learning (EE-ML) dataset
is available to the best of our knowledge. The EE-MRIO data is a high-quality
source for constructing ML-ready datasets. However, the existing dataset faces
limitations in terms of accessibility and resolution. Specifically, Eora
offers high-quality data but is closed-source and requires high-cost
purchasing (Lenzen et al., 2013). Global Trade Analysis Project (GTAP) covers
only five reference time steps with non-free access (Chepeliev, 2023). The
World Input-Output Database (WIOD) (Dietzenbacher et al., 2013) has been
questioned for its low resolution of temporal and spatial scope, whose latest
temporal coverage is 2016. Therefore, uniform problem formulations and
benchmark datasets need to be urgently developed. In summary, there exists a
big gap in the EE-ML cooperation, which is demonstrated in three aspects: the
inaccessibility of the dataset, the intensive data preprocessing with domain
knowledge are required and the lack of Benchmark ML datasets and models.

Figure 1: Environmental Extended Multi-Regional Input-Output (EE-MRIO) data
are represented in a high-dimensional matrix format, tracking the monetary
resource transfers between international sectors using input-output and demand
matrices. Additionally, it accounts for the environmental impacts of economic
activities through the Factor Accounting table.

The predominant Ecological Economics research framework, the Environmentally
Extended Multiregional Input-Output (EE-MRIO) analysis, effectively models
global economic interactions of sectors within a network structure (Leontief &
Strout, 1963; Wang et al., 2019; Sun et al., 2020a; Steinberger et al., 2012;
Jakob & Marschinski, 2013). As EE-MRIO describes the environmental footprint
for global economic activities, it has become the fundamental framework for EE
research, illustrated in Figure 1. EE-MRIO supports various studies such as
Structure Decomposition Analysis (SDA) and Index Decomposition Analysis (IDA)
to identify changes by decomposing key drivers (Hoekstra & Van den Bergh,
2003; Peters et al., 2017; Duan & Yan, 2019); monitoring embodied emissions
with resource transfer and sustainability evaluation of supply chain in global
trade (Meng et al., 2023; Tian et al., 2022; Wu et al., 2020; He & Hertwich,
2019; Li et al., 2020; Long et al., 2018; Sun et al., 2020a). Recently, ML
algorithms have been applied with EE-MRIO for several applications, such as
accurately identifying ecological hotspots and inefficiencies within the
global supply chain to optimise logistic paths for decreased carbon emissions
while considering cost-effectiveness (Akbari & Do, 2021). ML algorithms are
naturally suitable for learning multi-dimensional patterns and are utilised
for better sectoral sustainability assessment considering environmental,
economic and social impacts (Abdella et al., 2020; Nilashi et al., 2019).
Additionally, the ML algorithms extract the patterns from the original high-
dimensional space to latent feature space, and it is leveraged to extract the
critical paths of products’ energy, water, and emission footprints from giant
trading networks for supporting sustainable investment decisions (Ding et al.,
2022).

##  3 Methods

The architecture of the proposed ExioML dataset is illustrated in Figure 2. It
ends the challenges of data limitation and simplifies the EE-MRIO framework
for ML researchers in a ready-to-use manner. ExioML offers high spatiotemporal
resolution for 49 regions from 1995 to 2022 containing tabular and graphical
formats, supporting diverse ML research, such as time-series emissions
forecasting (Deb et al., 2017), factor analysis (Raihan et al., 2022; Zhang et
al., 2019; Duan & Yan, 2019; Acheampong & Boateng, 2019; Khan et al., 2020),
graph learning (Sun et al., 2020b) and clustering analysis (He et al., 2022;
Kijewska & Bluszcz, 2016), under a uniform framework. The details of ExioML
are summarised in Tablue 1.

Figure 2: Architecture of ExioML system derived from the open-source EE-MRIO
database, ExioBase 3.8.2. Each colour indicates an Eco-economics factor: value
added, employment, energy consumption and GHG emission. The system contains
Factor Accounting data describing heterogeneous sector features. The Footprint
Networks model the global trading network tracking resource transfer within
sectors. The data is presented into 2 categories: 200 products and 163
industries for 49 regions from 1995 to 2022 in the PxP and IxI datasets.

ExioML is derived from an open-source EE-MRIO dataset, ExioBase 3.8.2 (Stadler
et al., 2018; Stadler, 2021), a comprehensive source database covering 417
emission categories and 662 material and resource categories occupying over 40
GB of storage. ExioBase 3.8.2 is available in two versions based on different
sector classifications. Specifically, the first version is defined on a
product-by-product (PxP) basis, covering 200 products, while the second
version is defined on an industry-by-industry (IxI) basis, encompassing 163
industries. ExioML treats products and industries as different sector objects,
describes them using common features, and creates PxP and IxI into two
complementary datasets. Therefore, ExioML preserves the comprehensive
information of raw data sources. ExioML provides PxP and IxI data under a
similar distribution for self-contained validation. It enables future ML
research to evaluate the generalisation and robustness of ML models on two
datasets and reduces the effort to seek datasets under the same structures.

Table 1: Summaries of details of ExioML dataset. Characteristics | Description
---|---
Components | Factor accounting (Tabular data), Footprint Networks (Graph data)
Time frame | Covers 28 annual time steps from 1995 to 2022
Geographical coverage | 49 regions (44 countries and 5 rest of the world)
Sectors detail | 200 products in PxP, 163 industries in IxI
Key factors | Value added, employment, energy consumption, and GHG emission

Additionally, we open-sourced the well-documented ExioML development toolkit
to stimulate interdisciplinary corporations in three contributions. Firstly,
by providing the footprint calculation, the toolkit reduces the barrier for
new ML researchers without EE domain knowledge. Secondly, data storage and
computational demands are substantial due to high-dimensional matrix
operations. ExioML development toolkit utilises GPU accelerations and novelty
stores multi-dimensional networks in a single edge table, significantly
reducing the computing and storage. Thirdly, the toolkit provides a highly
flexible interface for querying the factors of interest from ExioBase 3.8.2.
MRIO features contain specific Eco-economics meanings, such as various
emissions types. Independent features should be carefully selected. Therefore,
four essential factors are identified by the EE research community: GHG
emissions, population, gross domestic product (GDP), and total primary energy
supply (TPES). The decomposition uses the Kaya Identity, a popular structural
decomposition method designed specifically for climate change (Peters et al.,
2017). This method breaks down carbon emissions into four indicators, which
are population ($P$), GDP per capita ($G/P$), energy intensity ($E/G$) and
carbon intensity ($F/E$):

$F=P\times\frac{G}{P}\times\frac{E}{G}\times\frac{F}{E}.$ (1)

Intensive data preprocessing, such as heavy data cleaning, structure
transformation, and Footprint Networks calculation, is required to transfer
the raw data into a standardized ML paradigm. Here are the construction
details of the ExioML database that utilises the MRIO framework. $Z$ is the
transaction matrix that indicates the global inter-industries flows within and
across $m$ regions. Each submatrix $Z^{rs}$ represents the trade from the
inter-regional requirement from region $r$ to region $s$ for each sector.
Similarly, $Y$ is the demand matrix similar. Initially, the column vector $x$
symbolizes the global economy’s output, with each element $x^{r}$ representing
the total output of region $r$. This could be determined by transaction matrix
$Z$, demand matrix $Y$ and $e$ is the summation vector:

$\begin{pmatrix}x^{1}\\\ x^{2}\\\ \vdots\\\ x^{m}\\\
\end{pmatrix}=\begin{pmatrix}Z^{11}&Z^{12}&\cdots&Z^{1m}\\\
Z^{21}&Z^{22}&\cdots&Z^{2m}\\\ \vdots&\vdots&\ddots&\vdots\\\
Z^{m1}&Z^{m2}&\cdots&Z^{mm}\end{pmatrix}e+\begin{pmatrix}Y^{11}&Y^{12}&\cdots&Y^{1m}\\\
Y^{21}&Y^{22}&\cdots&Y^{2m}\\\ \vdots&\vdots&\ddots&\vdots\\\
Y^{m1}&Y^{m2}&\cdots&Y^{mm}\end{pmatrix}e.$ (2)

Then, the direct requirement matrix $A$, indicative of technological
efficiency, is derived by multiplying the transaction matrix $Z$ with
$\hat{x}^{-1}$, which is the diagonalized and inverted vector of the global
economy’s output $x$:

$A=Z\hat{x}^{-1}.$ (3)

Further, the economy’s output vector $X$ is expressible via the Leontief
Matrix $L$:

$X=(I-A)^{-1}y=Ly.$ (4)

This framework can incorporate environmental accounting, such as energy
consumption and greenhouse gas (GHG) emissions, with the factor represented by
$F$. The coefficient $S$ normalizes this factor against the output $x$:

$S=F\hat{x}^{-1}.$ (5)

Finally, the Footprint Networks $D$ could be determined by:

$D=SLy.$ (6)

##  4 Technical Validation

GHG emissions are a crucial socio-economic measure for evaluating sectoral
sustainability, yet they remain highly uncertain in terms of quantification
(Sun & Huang, 2022; Riahi et al., 2007). Emissions are an indirect indicator,
unlike value-added, employment, or energy consumption, which can be directly
measured. The calculation of sectoral emissions depends on the efficiency of
raw materials and production systems, which vary widely across different
sectors. Consequently, sectors struggle to disclose their carbon emissions
accurately, impacting carbon transparency (Matisoff, 2013). Therefore, our
study demonstrates a multimodel sectoral GHG emissions regression task
leveraging categorical and numerical features to validate the usability of the
proposed ExioML. The model aims to automatically learn the underlying
relationships of Eco-economics factors to quantify sectoral GHG emissions.

The supervised regression task is to learn the mapping function
$f_{\theta}:X\mapsto Y$ from feature vector $x_{i}\in X$ to labels $y_{i}\in
Y$ on dataset $\mathcal{D}=\\{(x_{1},y_{1}),...,(x_{n},y_{n})\\}$. In this
case, we utilized the PxP and IxI as dataset $\mathcal{D}$ of ExioML’s Factor
Accounting table, comprising 221,307 and 179,185 instances after excluding
missing values, respectively. The feature vector $X$ is value-added,
employment and energy consumption, while GHG emission is set as the label $Y$.

Regression models could be categorized into shallow and deep learning models.
Deep learning in Eco-economics analysis, particularly on tabular data, remains
relatively underexplored (Gorishniy et al., 2021). We rigorously evaluated
different learning algorithms on tabular learning algorithms, including tree-
based models, MLP and Transformer architectures (Vaswani et al., 2017). The
distinction lies in their handling of categorical features: shallow models
employ Leave-One-Out Text Encoding due to their inability to process
categorical data natively (Zhang, 2016), whereas deep learning models learn
categorical embeddings directly from raw data. We implemented the deep
learning algorithms by PyTorch Tabular package (Joseph, 2021). Algorithms
included in GHG emission regression are as follows:

  * •

K-Nearnest-Neighbour (KNN): This non-parametric method predicts based on the
majority vote from its k nearest neighbours (Cover & Hart, 1967).

  * •

Ridge Regression (Ridge): A linear regression variant employing $\ell_{2}$
regularization to prevent overfitting (Hoerl & Kennard, 1970).

  * •

Decision Tree (DT): A feature splitting algorithm to tree flowchart structure
for decision making (Quinlan, 1986).

  * •

Random Forest (RF): Ensemble of multiple decision trees for robust prediction
(Breiman, 2001).

  * •

Grandient Boost Decision Tree (DBDT): Ensemble weak trees to improve the error
of the previous tree sequentially (Friedman, 2001).

  * •

Multi-Layer Perceptron (MLP): A deep artificial neural network employing
backpropagation with the capability of non-linear function approximation
(LeCun et al., 2015).

  * •

Gated Adaptive Network (GANDALF): GANDALF leverages Gated Feature Learning
Units (GFLU) for automated feature selection (Joseph & Raj, 2022).

  * •

Feature-Tokenizer Transformer (FTT): Novel transformer architecture applies a
tokenization mechanism on tabular features (Gorishniy et al., 2021).

This analysis assessed each model based on its mean square error and training
time. To ensure unbiased evaluation and prevent leakage of target information,
the dataset was partitioned into training, validation, and testing sets with
proportions of 64%, 16% and 20% to avoid label information leakage. Optimal
model parameters were determined through a random search across 30 trials on
the training and validation data. We conducted 10 experiments for each tuned
model hyperparameter with their performance on the test set. The experimental
procedures are outlined in Table 2. Data normalization was employed for DL
models to ensure stable training, although this did not significantly enhance
performance. In terms of implementation, the MPS framework was used to train
the deep models on Apple M3 Pro GPUs.

Table 2: Impletement detail. Data | Description | Setting | Description
---|---|---|---
# Train | 141,635 (PxP), 114,678 (IxI) | Epoch | 30
# Validation | 35,409 (PxP), 28,670 (IxI) | Batch size | 512
# Test | 44,262 (PxP), 35,837 (IxI) | Shallow | Sci-kit Learn
Encoding | Shallow models only | Deep | PyTorch Tabular
Normalisation | DL models only | Version | Python 3.11.7
Scaling | Log-transformation | GPU | Apple M3 Pro 18 GB

Table 3 summarized the model’s mean performance and standard deviation. The
primary finding is that deep learning models exhibit marginally lower mean
squared errors than ensemble counterparts. GANDALF shows the most effective
performance in PxP and IxI data. Ensemble methods, GBDT and RF, emerge with
competitive accuracy and less demand for computational resources. Although
deep models benefit from GPU acceleration, their training time is
significantly longer than shallow models. We demonstrated the evaluation of
various machine learning models on Factor Accounting datasets focused on
sectoral GHG emission prediction through regression tasks, serving as
effective baselines with low mean square error performance.

Table 3: Result for models with standard deviation for 10 runs and the top results are in bold. | PxP | IxI
---|---|---
Model | MSE | Time (s) | MSE | Time (s)
KNN | 1.071 ± 0.010 | 0.035 ± 0.001 | 1.151 ± 0.018 | 0.026 ± 0.001
Ridge | 2.265 ± 0.000 | 0.005 ± 0.002 | 2.514 ± 0.000 | 0.004 ± 0.002
DT | 0.926 ± 0.051 | 0.316 ± 0.017 | 0.848 ± 0.070 | 0.254 ± 0.019
RF | 0.356 ± 0.004 | 21.521 ± 0.157 | 0.302 ± 0.004 | 16.511 ± 0.060
GBDT | 0.234 ± 0.006 | 30.276 ± 0.224 | 0.219 ± 0.007 | 32.847 ± 0.388
MLP | 0.226 ± 0.007 | 219.218 ± 0.904 | 0.250 ± 0.092 | 205.051 ± 24.309
GANDALF | 0.204 ± 0.010 | 352.756 ± 7.036 | 0.189 ± 0.007 | 383.119 ± 3.664
FTT | 0.330 ± 0.007 | 330.578 ± 1.527 | 0.302 ± 0.023 | 468.911 ± 7.329

##  5 Conclusion

To summarize, this research introduces the first high-quality EE-ML benchmark
dataset integrated with the EE-MRIO framework, a powerful analysis tool in
ecological economics research. The novel ExioML dataset and development
toolkit address three significant challenges in sustainability research and
establish benchmark data and algorithms for future studies. Firstly, ExioML
overcomes data access limitations by providing high-resolution resources.
Secondly, it streamlines the complexity of the EE-MRIO framework with GPU-
accelerated computations and efficient storage management, offering high-level
factor customization for future researchers. Finally, we demonstrated a
sectoral GHG emission prediction task to the usability of the proposed dataset
on various machine learning models and achieved low mean square errors.

ExioML fosters interdisciplinary collaboration between ecological economics
and machine learning, laying a solid foundation for further research in areas
such as supply chain optimization, sustainability assessment, and footprint
prediction. This contributes to formulating effective climate policies and
making sustainable investment decisions.

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##  Appendix A Competing interests

The authors declare no competing interests.

##  Appendix B Author Contributions

Y. Guo designed the study and produced the dataset, visualisations, and
regression models for technical validation. J. Ma supervised the project. Both
authors participated in writing the paper. T. Guan participated in the project
design discussion and helped improve the paper draft.

##  Appendix C Code Availability

The ExioML development toolkit by Python and the regression model used for
validation are available on the GitHub repository
(https://github.com/Yvnminc/ExioML). And the ExioML data is hosted by Zenodo
(https://zenodo.org/records/10604610).

##  Appendix D Data Records & Visualisation

ExioML supports graph and tabular structure learning algorithms using
Footprint Networks and Factor Accounting table. The factors included in PxP
and IxI in ExioML are detailed in Table 4.

Table 4: Feature of PxP and IxI dataset in ExioML. Attribute name | Type | Description
---|---|---
Region | Categorical | Region code (e.g. AU, US, CN)
Sector | Categorical | Product or industry (e.g. biogasoline, construction)
Year | Numerical | Timestep (e.g. 1995, 2022)
Value added | Numerical | GDP expressed in millions of Euros
Employment | Numerical | Population engaged in thousands of persons
Energy carrier net total | Numerical | Sum of all energy carriers in Terajoules
GHG emission | Numerical | GHG emissions in kilograms of CO2 equivalent

The Footprint Networks model the high-dimensional global trading networks,
capturing its economic, social, and environmental impacts illustrated in
Figure 3. This network is structured as a directed graph, where the
directionality represents the sectoral input-output relationships, delineating
sectors by their roles as sources (exporting) and targets (importing). The
basic element in the ExioML Footprint Networks is international trade across
different sectors with different features such as value-added, emission
amount, and energy input. The Footprint Networks’ potential pathway impact is
learning the dependency of sectors in the global supply chain to identify
critical sectors and paths for sustainability management and optimisation.

Figure 3: Dynamic Global Trade Footprint Networks from 1995 to 2022. This
visualization employs a circular layout to depict the evolving trade patterns
over time. Nodes represent sectors indicated by colours. Edges indicate
resource transfers, with colours reflecting the source regions. The diagram
highlights significant shifts in primary trade sources and target sectors,
illustrating the dynamic structural changes in the global trading network.

The second part of ExioML is the Factor Accounting table, which shares the
common features with the Footprint Networks and summarises the total
heterogeneous characteristics of various sectors, which could be leveraged to
perform sectoral sustainability assessment. Figure 4 describes the boxplot of
the top 10 sectors respective to four selected factors, and Figure 5 displays
the pairplot of sector scatters. Log transformation is applied to re-scale the
Eco-economics data due to inherent skewness.

Figure 4: Boxplots for top 10 sectors with largest value-added, employment,
GHG emission and energy usage in PxP Factor Accounting table. The x-axis
represents indicators transformed using a logarithmic scale, while the y-axis
lists sector names. These boxplots reveal the high skewness in sector
distributions across different regions. Figure 5: The diagonal subfigures in
the pair plot display the sectoral distribution with respect to four selected
factors. Off-diagonal subfigures present the pairwise scatter plots, where
each point represents a sector and is color-coded by region. The plot reveals
high relative pairwise correlations among these factors.

Storing large multi-dimensional Footprint Networks poses a significant
challenge. For example, a single factor’s connections for one year amount to
63,792,169, consuming approximately 1 GB of storage. We addressed this
challenge by leveraging the characteristics of the global trading network. By
representing the network in an edge table, we stored the Multi-dimensional
Footprint Networks in a single file, significantly reducing computing and
storage requirements. Given that the global trading network is scale-free and
edge weights follow a power-law distribution (Foster, 2005), the network is
overall sparse, with connections concentrated around dominant nodes. We
transformed the graph adjacency matrix into an edge table in tabular format,
selecting the top 1,000,000 connections per year. After performing an inner
join with the selected factors, around 300,000 edges per year remained. The
total storage consumption for the PxP and IxI Footprint Networks is 1.43 GB
and 1.34 GB, respectively. The Factor Accounting table requires even less
computation and storage, consuming approximately 20 MB.

##  Appendix E Hyperparamter Tuning

A random search of hyperparameters was conducted over 30 trials within a
defined parameter grid. The range of parameters tested and the best parameters
identified are summarized in Table 5. The search grid was heuristically small
and based on model characteristics and previous studies to optimize
efficiency. The tuning process for each ensemble model took about 10 to 20
minutes per dataset, whereas for each DL model, it took roughly 2 to 3 hours.

Table 5: Parameter grid for tuning and best parameter found for PxP and IxI. Model | Parameter range | Best PxP | Best IxI
---|---|---|---
KNN |  |  |
# Neighbours | Range(1, 50, 2) | 5 | 3
Weights | Uniform, Distance | Distance | Distance
Metric | Euclidean, Manhattan | Manhattan | Manhattan
Ridge |  |  |
Alpha | 0, 0.001, 0.01, 0.1, 0, 1, 10, 100 | 0 | 100
DT |  |  |
Max depth | Range(1, 50, 2) | 46 | 46
Min samples leaf | 1, 2, 3 | 3 | 1
Min samples split | 2, 4, 6, 8 | 4 | 6
Max features | Sqrt, Log2 | Sqrt | Log2
RF |  |  |
Max depth | Range(1, 50, 2) | 26 | 26
Min samples leaf | 1, 2, 3 | 1 | 1
Min samples split | 2, 4, 6, 8 | 2 | 2
Max features | Sqrt, Log2 | Sqrt | Log2
# Estimators | 50, 100, 150 | 100 | 100
GBDT |  |  |
Max depth | Range(1, 50, 2) | 16 | 31
Min samples leaf | 1, 2, 3 | 3 | 3
Min samples split | 2, 4, 6, 8 | 8 | 2
Max features | Sqrt, Log2 | Log2 | Log2
# Estimators | 50, 100, 150 | 100 | 100
Learning rate | 0.01, 0.1, 1 | 0.1 | 0.1
MLP |  |  |
Layers | 256-128-64, 128-64-32, 64-32-16 | 256-128-64 | 256-128-64
Dropout | 0, 0.05, 0.1 | 0 | 0
Learning rate | 0.001, 0.01 | 0.01 | 0.01
GANDALF |  |  |
GFLU stages | 1,2,3,4,5 | 4 | 4
GFLU dropout | 0, 0.05, 0.1 | 0 | 0
Feature sparsity | 0, 0.05, 0.1 | 0 | 0
Learning rate | 0.001, 0.01 | 0.01 | 0.01
FTT |  |  |
# Heads | 4, 8 | 8 | 8
# Attention blocks | 1, 2, 3, 4 | 2 | 3
# Multiplier | 1, 2, 3, 4 | 4 | 1
Learning rate | 0.001, 0.01 | 0.01 | 0.01