From e7bd9ddc41d5fb04ba16f010171abcd1a7155772 Mon Sep 17 00:00:00 2001 From: Alex Date: Fri, 9 Feb 2024 19:08:41 -0500 Subject: [PATCH] init slide deck mlforscience udem --- slides/mlforscience-udem-feb24.md | 911 ++++++++++++++++++++++++++++++ 1 file changed, 911 insertions(+) create mode 100644 slides/mlforscience-udem-feb24.md diff --git a/slides/mlforscience-udem-feb24.md b/slides/mlforscience-udem-feb24.md new file mode 100644 index 00000000..3a9f9fb4 --- /dev/null +++ b/slides/mlforscience-udem-feb24.md @@ -0,0 +1,911 @@ +--- +layout: slides_mila_owl +title: "Machine learning for science: tackling climate and health challenges" +--- + +name: mlforscience-udem-feb24 +class: title, middle + +## Machine learning for science +### Tackling climate and health challenges + +
+ +## Apprentissage automatique pour les sciences +### S'attaquer à la crise climatique et aux défis en santé + +Alex Hernández-García (he/il/él) + +.center[ +UdeM +     +Mila +] + +.smaller[.footer[ +Slides: [alexhernandezgarcia.github.io/slides/{{ name }}](https://alexhernandezgarcia.github.io/slides/{{ name }}) +]] + +--- + +## Collaborators + +.left-column[ +* Nikita Saxena +* Moksh Jain +* Chenghao Liu +* Yoshua Bengio +* ... +] + +.right-column[ +* Kolya Malkin +* Salem Lahlou +* Alexandra Volokhova +* Emmanuel Bengio +* ... +] + +--- + +## Outline + +* Part 1: Motivation: Why scientific discovery? + * Challenges, limitations and opportunities for machine learning +* Part 2: A brief introduction to GFlowNets +* Part 3: Multi-fidelity active learning with GFlowNets + +--- + +name: title +class: title, middle + +## Motivation: Why scientific discovery? +### Part 1 + +.center[![:scale 30%](../assets/images/slides/climatechange/demo.jpg)] + +--- + +## Why scientific discovery? + +.context[Climate change is a major challenge for humanity.] + +.left-column-66[.center[ +
+ Historical global average temperature and the influence of modern humans +
.smaller[Modelled and observed global average temperatures in the last 2 millenia (source graphic: The Guardian.)]
+
+]] + +.right-column-33[ +Consequences: +* Melting glaciers and polar ice +* Sea level rise +* Heatwaves +* Floods +* Droughts +* Wildfires +* ... +] + +??? + +* Flash floods kill **5,000** people per year. +* Sea levels are expected to rise by **2 metres** by the end of the century +* Rising sea levels could disrupt the lives of **1 billion people** by the end of 2050. +* As much as **40% of the Amazon** forest is at risk of becoming a savanna. +* In 2015, forest fires claimed roughly **980 000 $km^2$** of the world’s forest. +* Forest fires emmitted **~1.8 Gt of CO2** in 2019. + + +--- + +## Why scientific discovery? + +.context[Climate change is a major challenge for humanity.] + +.center[ +
+ IPCC 2022 - Scenarios +
Median global warming across modelled scenarios. Adapted from IPCC Sixth Assessment Report, 2022
+
+] + +-- + +.conclusion["The evidence is clear: the time for action is now." .smaller[IPCC Sixth Assessment Report, 2022]] + +??? + +* Category C1: scenarios that limit warming to 1.5°C in 2100 with a likelihood of greater than 50%, and reach or exceed warming of 1.5°C during the 21st century with a likelihood of 67% or less. +* Category C2: same as C1 but exceed warming of 1.5°C during the 21st century with a likelihood of _greater_ than 67%. +* Category C3: scenarios that limit peak warming to 2°C throughout the 21st century with a likelihood of greater than 67% +* Category C8: scenarios that exceed warming of 4°C during the 21st century with a likelihood of 50% or greater. + +--- + +## Why scientific discovery? + +.context["The time for action is now"] + +> "Limiting global warming will require major transitions in the energy sector. This will involve a substantial reduction in fossil fuel use, widespread electrification, .highlight1[improved energy efficiency, and use of alternative fuels (such as hydrogen)]." .cite[IPCC Sixth Assessment Report, 2022] + +> "Net-zero CO2 emissions from the industrial sector are challenging but possible. Reducing industry emissions will entail coordinated action throughout value chains to promote all mitigation options, including demand management, .highlight1[energy and materials efficiency, circular material flows], as well as abatement technologies and transformational changes in production processes." .cite[IPCC Sixth Assessment Report, 2022] + +-- + +
+ +.conclusion[Mitigation of the climate crisis requires transformational changes in the energy and materials efficiency.] + +--- + +## Why scientific discovery? +### The potential of better materials + +.context[The climate crisis demands more efficient materials.] + +* Improving material efficiency can reduce 0.93 ($\pm$ 0.23) GtCO₂-eq per year. +* Fuel switching can reduce 2.1 ($\pm$ 0.52) GtCO₂-eq per year, only in the industry sector. +* Carbon capture and storage can reduce 0.54 ($\pm$ 0.27) GtCO₂-eq per year in the energy sector. + +.right[.cite[IPCC Sixth Assessment Report (2022)]] + +.smaller[.footnote[† Global anthropogenic emissions in 2019 were estimated in 59 ($\pm$ 6.6) GtCO₂-eq.]] + +--- + +count: false + +## Why scientific discovery? +### The potential of better materials + +.context[The climate crisis demands more efficient materials.] + +* Improving material efficiency can reduce 0.93 ($\pm$ 0.23) GtCO₂-eq per year. +* Fuel switching can reduce 2.1 ($\pm$ 0.52) GtCO₂-eq per year, only in the industry sector. +* Carbon capture and storage can reduce 0.54 ($\pm$ 0.27) GtCO₂-eq per year in the energy sector. + +.right[.cite[IPCC Sixth Assessment Report (2022)]] + +What are better, new materials needed for? + +* Electrocatalysts for fuel cells, hydrogen storage, industrial chemical reactions, carbon capture, etc. +* Solid electrolytes for batteries. +* Thin film materials for photovoltaics. +* ... + +.smaller[.footnote[† Global anthropogenic emissions in 2019 were estimated in 59 ($\pm$ 6.6) GtCO₂-eq.]] + +--- + +## Traditional scientific discovery loop + +.context35[The climate crisis demands accelerating scientific discoveries.] + +.right-column-66[
.center[![:scale 90%](../assets/images/slides/materials/activelearning_noml.png)]] + +.left-column-33[ +The .highlight1[traditional pipeline] for scientific discovery (paradigms 1-3): +* relies on .highlight1[highly specialised human expertise], +* it is .highlight1[time-consuming] and +* .highlight1[financially and computationally expensive]. +] + +--- + +count: false + +## Machine learning in the loop + +.context35[The traditional scientific discovery loop is too slow.] + +.right-column-66[
.center[![:scale 90%](../assets/images/slides/materials/activelearning_ml.png)]] + +.left-column-33[ +A .highlight1[machine learning model] can be: +* trained with data from _real-world_ experiments and +* used to quickly and cheaply evaluate queries +] + +--- + +count: false + +## Machine learning in the loop + +.context35[The traditional scientific discovery loop is too slow.] + +.right-column-66[
.center[![:scale 90%](../assets/images/slides/materials/activelearning_ml.png)]] + +.left-column-33[ +A .highlight1[machine learning model] can be: +* trained with data from _real-world_ experiments and +* used to quickly and cheaply evaluate queries + +.conclusion[A machine learning model replacing real-world experiments can _only_ provide _linear_ gains.] + +.conclusion[Not enough if the search space is very large ($10^{180}$ stable materials)] +] + +--- + +count: false + +## _Generative_ machine learning in the loop + +.context[Can we do better than _linear_?
An agent in the loop.] + +.right-column-66[
.center[![:scale 90%](../assets/images/slides/materials/activelearning_agent.png)]] + +.left-column-33[ +A .highlight1[machine learning **agent**] in the loop could (ideally): +* .highlight1[learn structure] from the available data, +* .highlight1[generalise] to unexplored regions of the search space and +* .highlight1[build better queries] +] + +--- + +count: false + +## _Generative_ machine learning in the loop + +.context[Can we do better than _linear_?
An agent in the loop.] + +.right-column-66[
.center[![:scale 90%](../assets/images/slides/materials/activelearning_agent.png)]] + +.left-column-33[ +A .highlight1[machine learning **agent**] in the loop could (ideally): +* .highlight1[learn structure] from the available data, +* .highlight1[generalise] to unexplored regions of the search space and +* .highlight1[build better queries] + +.conclusion[A successful AL pipeline with an ML agent in the loop can provide _exponential_ gains.] +] + +--- + +count: false + +## _Generative_ machine learning in the loop + +.context[GFlowNet as the agent.] + +.right-column-66[
.center[![:scale 90%](../assets/images/slides/materials/activelearning_hl-gfn.png)]] + +.left-column-33[ +A .highlight1[machine learning **agent**] in the loop could (ideally): +* .highlight1[learn structure] from the available data, +* .highlight1[generalise] to unexplored regions of the search space and +* .highlight1[build better queries] + +.conclusion[A successful AL pipeline with an ML agent in the loop can provide _exponential_ gains.] +] + +.references[ +Jain et al. [GFlowNets for AI-Driven Scientific Discovery](https://arxiv.org/abs/2302.00615). Digital Discovery, Royal Society of Chemistry, 2023. +] + +--- + +## Machine learning for scientific discovery +### Challenges and limitations of existing methods + +.highlight1[Challenge]: very large search spaces. + +-- + +→ Need for .highlight2[efficient search and generalisation] of underlying structure. + +-- + +.highlight1[Challenge]: underspecification of objective functions or metrics. + +-- + +→ Need for .highlight2[diverse] candidates. + +-- + +.highlight1[Limitation]: Reinforcement learning and MCMC methods are good at optimisation but poor at mode mixing. + +-- + +→ Need for .highlight2[_multi-modal_ optimisation]. + +--- + +name: title +class: title, middle + +## A brief introduction to GFlowNets +### Part 2 + +.center[![:scale 30%](../assets/images/slides/gfn-seq-design/flownet.gif)] + +--- + +## GFlowNet in a nutshell + +
+Given a reward or objective function $R(x)$, GFlowNet can be seen a generative model trained to sample objects $x \in \cal X$ according to .highlight1[a sampling policy $\pi(x)$ proportional to the reward $R(x)$]: $\pi(x) \propto R(x)$ + +-- + +→ Sampling proportionally to the reward function induces .highlight1[multi-modal search and diversity]. + +-- + +.left-column[ +The policy $\pi_{\theta}(x)$ is modelled by a deep neural network, parameterised by $\theta$, thus providing .highlight1[amortised inference]. + +→ Amortised inference can be thought of as _exploration with memory_, which induces .highlight1[systematic generalisation]. +] + +.right-column[ +.center[![:scale 65%](../assets/images/slides/gflownet/mode_generalization.png)] +] + +--- + +## GFlowNet in a nutshell + +* Objects $x \in \cal X$ are constructed through a sequence of steps $\tau$ from an action space $\cal A$. +* At each step of the trajectory $\tau=(s_0\rightarrow s_1 \rightarrow \dots \rightarrow s_f)$, we get a partially constructed object $s$ in state space $\cal S$. +* This induces a directed acyclic graph (DAG) $\mathcal{G}=(\mathcal{S},\mathcal{A})$, with all possible constructions in the domain. + +.center[![:scale 50%](../assets/images/slides/gflownet/flownet.png)] + +-- + +.conclusion[This terminology is reminiscent of reinforcement learning.] + +--- + +## An intuitive toy example + +Task: find arrangements of Tetris pieces on the board that minimise the empty space. + +.left-column[ +.center[![:scale 20%](../assets/images/slides/tetris/board_empty.png)] +] + +.right-column[ +![:scale 15%](../assets/images/slides/tetris/piece_J.png) ![:scale 15%](../assets/images/slides/tetris/piece_L.png) ![:scale 15%](../assets/images/slides/tetris/piece_O.png) +] + +-- + +.conclusion[This task resembles designing DNA sequences or molecules or materials via fragments, with the objective of optimising certain properties.] + +--- + +## An intuitive toy example + +Task: find arrangements of Tetris pieces on the board that minimise the empty space. + +.columns-3-left[.center[ +
+ State space +
State space $\cal S$
+
+]] + +.columns-3-center[.center[ +
+ Action space +
Action space $\cal A$
+
+]] + +.columns-3-right[.center[ +
+
+
+ s0 +
$s_0$
+
+
+
+ $\rightarrow$ +
+
+
+ s1 +
$s_1$
+
+
+
+ $\rightarrow$ +
+
+
+ s2 +
$s_2$
+
+
+
+]] + +--- + +## An intuitive toy example + +Task: find arrangements of Tetris pieces on the board that minimise the empty space. + +.center[ +
+
+
+ Score 0/12 +
Score: 0/12
+
+
+
+
+ Score 4/12 +
Score: 4/12
+
+
+
+
+ Score 8/12 +
Score: 8/12
+
+
+
+
+ Score 12/12 +
Score: 12/12
+
+
+
+] + +--- + +## An intuitive toy example + +Task: find arrangements of Tetris pieces on the board that minimise the empty space. + +.center[ +
+
+
+ Score 0/12 +
Score: 12/12
+
+
+
+
+ Score 4/12 +
Score: 12/12
+
+
+
+
+ Score 8/12 +
Score: 12/12
+
+
+
+
+ Score 12/12 +
Score: 12/12
+
+
+
+
+ Score 12/12 +
Score: 12/12
+
+
+
+] + +.conclusion[The _reward function_ of this task has multiple modes. With a larger board and more pieces, the number of combinations and modes grow exponentially and the task of efficiently finding them is non-trivial for machine learning models.] + +--- + +## GFlowNet flows + +.context[The edges or transitions in the DAG can be quantified by their _flow_.] + +* Analogous to water-flow in pipes. +* Trajectory Flow $F(\tau)$ denotes probability mass assigned to trajectory $\tau$. +* State Flow $F(s)$ is the flow of all trajectories passing through the state $s$. +* Edge Flow $F(s\rightarrow s')$ is the flow through a particular edge $s\rightarrow s'$. +* Forward Policy $P_F$: $P\_F(s'|s) = \frac{F(s\rightarrow s')}{F(s)}$ +* Backward Policy $P_B$: $P\_B(s|s') = \frac{F(s\rightarrow s')}{F(s')}$ + +.center[![:scale 30%](../assets/images/slides/gfn-seq-design/flownet.gif)] + +.references[ +Bengio et al. [Flow network based generative models for non-iterative diverse candidate generation](https://arxiv.org/abs/2106.04399), NeurIPS, 2021. +] + +??? + +Not to be confused with normalizing flows! + +--- + +## Principle of conservation as a training objective + +.right-column-33[.center[![:scale 100%](../assets/images/slides/gfn-seq-design/flownet.gif)]] + +.left-column-66[ +**Consistent Flow**: Flow $F$ satisfies the _flow consistency equation_ +$$\sum\_{s' \in \text{Parents}(s)} F\_\theta(s' \rightarrow s) = \sum\_{s' \in \text{Children}(s)} F\_\theta(s \rightarrow s')$$ + +**Theorem**: For a consistent flow $F$ with terminal flow set as the reward $F(x\rightarrow s_f)=R(x)$, the forward policy samples $x$ proportionally to $R(x)$: +$$\pi(x) = \frac{R(x)}{Z}\propto R(x)$$ + +**Corollary**: The flow at $s_0$, $F(s_0)$ is the partition function $Z$! +] + +.references[ +Bengio et al. [Flow network based generative models for non-iterative diverse candidate generation](https://arxiv.org/abs/2106.04399), NeurIPS, 2021. +] + +--- + +## Principle of conservation as a training objective + +

+$$\sum\_{s' \in \text{Parent}(s)} F\_\theta(s' \rightarrow s) = \sum\_{s'' \in \text{Child}(s)} F\_\theta(s \rightarrow s')$$ +

+* **Flow Matching Objective**: $$\mathcal{L}\_{FM}(s; \theta) = \left(\log \frac{\sum\_{s'\in \text{Parent}(s)} F\_\theta(s'{\rightarrow} s)}{\sum\_{s'' \in \text{Child}(s)}F\_\theta(s{\rightarrow} s'')}\right)^2$$ +* **Trajectory Balance** (better credit assignment): $$\mathcal{L}\_{TB} (\tau;\theta) = \left(\log \frac{Z\_\theta \prod\_{s{\rightarrow} s' \in \tau}P\_{F\_\theta}(s'|s)}{R(x)\prod\_{s\rightarrow s' \in \tau} P\_{B\_\theta}(s|s') }\right)^2$$ + +--- + +## Results +### Tetris GFlowNets + +.context[If the model is sufficiently trained, the sampling policy $\pi(x)$ should be proportional to the reward $R(x)$: $\pi(x) \propto R(x)$] + +
+ +.center[ +
+
+
+ Score 0/12 +
$\pi(x) = 8.12~\%$
+
+
+
+
+ Score 4/12 +
$\pi(x) = 8.96~\%$
+
+
+
+
+ Score 8/12 +
$\pi(x) = 8.61~\%$
+
+
+
+
+ Score 12/12 +
$\pi(x) = 9.16~\%$
+
+
+
+
+ Score 12/12 +
$\pi(x) = 8.39~\%$
+
+
+
+] + +After training, GFlowNet samples multiple (diverse) modes with high probability. + +.footnote[The energy function $\varepsilon(x)$ is the fraction of the board occupied by pieces and the reward function is $R(X) = \varepsilon(x)^4$ to disproportionally favour the discovery of modes.] + +--- + +## Results +### Hyper-grid and molecule fragments + +.context[GFlowNet has been successfully trained in other toy and practically relevant tasks. .cite[(Bengio et al., 2019)]] + +.columns-3-left[ +.highlight1[Hyper-grid]: The action space is in which dimension to move and the reward function has high reward in the corners. + +.center[ +![:scale 90%](../assets/images/slides/gflownet/hypergrid_states_visited.png) +] +] + +-- + +.columns-3-center[ +.highlight1[Small molecules]: The action space is molecular fragments and the reward function is the binding energy to a particular protein. + +.center[ +![:scale 80%](../assets/images/slides/gflownet/molecules_states_visited.png) +] +] + +-- + +.columns-3-right[ +.highlight1[Active learning with molecules]: Multi-round active learning with a limited oracle budget. + +.center[ +![:scale 90%](../assets/images/slides/gflownet/molecules_al_topkreward.png) +] +] + +-- + +.conclusion[GFlowNet is able to efficiently explore the search space and generalise to unseen modes of the reward.] + +--- + +## GFlowNet extensions +### Multi-objective GFlowNets + +We have extended GFlowNets to handle multi-objective optimisation and not only cover the Pareto front but also sample diverse objects at each pointin the Pareto front. + +.center[ +![:scale 30%](../assets/images/slides/gflownet/mogfn_pareto_front.png) +![:scale 30%](../assets/images/slides/gflownet/mogfn_al.png)] + +.references[ +Jain et al. [Multi-Objective GFlowNets](https://arxiv.org/abs/2210.12765), ICML, 2023. +] + +--- + +## GFlowNet extensions +### Continuous GFlowNets + +We have recently generalised the theory and implementation of GFlowNets to encompass both discrete and continuous or hybrid state spaces. + +.center[ +![:scale 30%](../assets/images/slides/gflownet/kde_reward_molecule.png) +![:scale 30%](../assets/images/slides/gflownet/kde_gfn_molecule.png)] + +.references[ +Lahlou et al. [A Theory of Continuous Generative Flow Networks](https://arxiv.org/abs/2301.12594), ICML, 2023. +] + +--- + +name: title +class: title, middle + +## Multi-fidelity active learning with GFlowNets +### Part 3 + +.center[![:scale 30%](../assets/images/slides/mfal/multiple_oracles.png)] + +--- + +## Why multi-fidelity? + +In many areas of scientific applications we have access to multiple approximations of the objective function. + +For example, for material discovery: + +* Synthesis of a material and characterisation of a property in the lab +* Density Functional Theory (DFT) +* An ensemble of large graph neural networks trained on DFT data +* An efficient, smaller neural network + +-- + +However, current multi-fidelity methods struggle with structured, large, high-dimensional search spaces and lack **diversity**. + +--- + +## Multi-fidelity active learning with GFlowNets + +.center[![:scale 100%](../assets/images/slides/mfal/mfal_bgwhite.png)] + +--- + +## Multi-fidelity surrogate models + +* Small (synthetic) tasks: exact Gaussian Processes +* Larger-scale, benchmark tasks: Deep Kernel Learning with stochastic variational Gaussian processes + +Multi-fidelity kernel learning: + +$$K(x, \tilde{x}, m, \tilde{m}) = K_1(x, \tilde{x}) \times K_2(m, \tilde{m})$$ + +* $K_1$: RBF kernel +* $K_2$: Downsampling kernel + +.references[ +* Wilson, Hu et al. [Deep Kernel Learning](https://arxiv.org/abs/1511.02222), AISTATS, 2016. +* Wu et al. [Practical multi-fidelity Bayesian optimization for hyperparameter tuning](https://arxiv.org/abs/1903.04703) , UAI, 2019. +] + +--- + +## Multi-fidelity acquisition function +### Maximum Entropy Search (MES) + +MES it aims to maximise the mutual information between .hihglight1[the value] of the objective function $f$ when choosing point *x* and the maximum of the objective function, $f^{\star}$ (instead of considering the `arg max`). + +The multi-fidelity variant is designed to select the candidate $x$ and the fidelity $m$ that maximise the mutual information between $f_M^\star$ and the oracle at fidelity $m$, $f_m$ , weighted by the cost of the oracle $\lambda_m$. + +$$\alpha(x, m) = \frac{1}{\lambda_{m}} I(f_M^\star; f_m | \mathcal{D})$$ + +.references[ +* Moss et al. [GIBBON: General-purpose Information-Based Bayesian OptimisatioN](https://arxiv.org/abs/2102.03324), JMLR, 2021. +] + +--- + +## Multi-fidelity GFlowNets (MF-GFN) + +Given a baseline GFlowNet with state space $\mathcal{S}$ and action space $\mathcal{A}$, we augment the state space with a new dimension for the fidelity $\mathcal{M'} = \{0, 1, 2, \ldots, M\}$ (including $m = 0$, which corresponds to unset fidelity). + +The set of allowed transitions $\mathcal{A}_M$ is augmented such that a fidelity $m > 0$ of a trajectory must be selected once, and only once, from any intermediate state. This is meant to provide flexibility and improve generalisation. + +Finished trajectories are the concatenation of an object $x$ and the fidelity $m$. + +GFlowNet is trained with the acquisition function $\alpha(x, m)$ as reward function. + +--- + +## Experiments +### Baselines + +* .highlight1[SF-GFN]: GFlowNet with highest fidelity oracle to establish a benchmark for performance without considering the cost-accuracy trade-offs. +* .highlight1[Random fid. GFN]: GFlowNet with random fidelities, that is a variant of SF-GFN where the candidates are generated with the GFlowNet but the fidelities are picked randomly and a multi-fidelity acquisition function is used, to investigate the benefit of deciding the fidelity with GFlowNets. +* .highlight1[Random]: Quasi-random approach where the candidates and fidelities are picked randomly and the top $(x, m)$ pairs scored by the acquisition function are queried. +* .highlight1[MF-PPO]: Instantiation of multi-fidelity Bayesian optimisation where the acquisition function is optimised using proximal policy optimisation (reinforcement learning). + +--- + +## Synthetic tasks: Branin and Hartmann + +.highlight1[Branin]: $100 \times 100$ grid, 3 oracles (from the BO literature). + +.highlight1[Hartmann]: 6D grid of length 10, 3 oracles (from the BO literature). + +.left-column[.center[ +
+ Branin +
Branin task
+
+]] + +.right-column[.center[ +
+ Hartmann +
Hartmann task
+
+]] + +--- + +## DNA aptamers and antimicrobial peptides (AMP) + +.highlight1[DNA]: GFlowNet adds one nucleobase (`A`, `T`, `C`, `G`) at a time up to length 30. This yields a design space of size $|\mathcal{X}| = 4^{30}$. The objective function is the free energy estimated by NUPACK. The (simulated) lower fidelity oracle is a transformer trained with 1 million sequences. + +.highlight1[AMP]: Protein sequences with variable length (max. 50). The oracles are 3 ML models trained with different subsets of data. + +.left-column[.center[ +
+ DNA +
DNA task
+
+]] + +.right-column[.center[ +
+ AMP +
AMP task
+
+]] + +--- + +## Small molecules + +More realistic experiments, with oracles that correlate with experimental results as approximations of the scoring function. The costs reflect the computational demands of each oracle (1, 3, 7). + +.left-column[.center[ +
+ Ionisation potential +
Ionisation potential task
+
+]] + +.right-column[.center[ +
+ Electron affinity +
Electron affinity task
+
+]] + +--- + +## How does multi-fidelity help? + +.context[Visualisation of results on the 2D Branin function.] + +.center[![:scale 50%](../assets/images/slides/mfal/branin_samples_per_fid.png)] + +--- + +name: title +class: title, middle + +## Summary and conclusions + +.center[![:scale 30%](../assets/images/slides/misc/conclusion.png)] + +--- + +## Summary and conclusions + +* Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires .highlight1[accelerating the pace of scientific discovery]. +* Current AI tools are not enough to truly utilize all the information and resources at our disposal. +* AI-driven scientific discovery demands learning methods that can .highlight1[efficiently discover diverse candidates in very large, multi-modal search spaces]. +* .highlight1[GFlowNet] is a learning method for amortised inference that can sample proportionally to a reward function. +* .highlight1[Multi-fidelity active learning with GFlowNets] enables .highlight1[cost-effective exploration] of large, high-dimensional and structured spaces, and discovers multiple, diverse modes of black-box score functions. + +.references[ +* Hernandez-Garcia, Saxena et al. [Multi-fidelity active learning with GFlowNets](https://arxiv.org/abs/2306.11715). arXiv 2306.11715, 2023. +* Jain et al. [GFlowNets for AI-Driven Scientific Discovery](https://arxiv.org/abs/2302.00615). Digital Discovery, Royal Society of Chemistry, 2023. +* Jain et al. [Biological Sequence Design with GFlowNets](https://arxiv.org/abs/2203.04115), ICML, 2022. +] + +-- + +.highlight2[Open source code]: [github.com/alexhernandezgarcia/gflownet](https://github.com/alexhernandezgarcia/gflownet) + +--- + +## Acknowledgements + +.left-column[ +* Nikita Saxena (Mila) - equivalent contribution +* Moksh Jain (Mila) +* Chenghao Liu (Mila) +* Yoshua Bengio (Mila) +] + +.right-column[ +* IVADO +* CIFAR +] + + +--- + +name: title +class: title, middle + +## Thanks! Questions? + +![:scale 40%](../assets/images/slides/mfal/mfal_bgwhite.png) + +Alex Hernández-García (he/il/él) + +.center[ +Mila +     +UdeM +] + +.footer[[alexhernandezgarcia.github.io](https://alexhernandezgarcia.github.io/) | [alex.hernandez-garcia@mila.quebec](mailto:alex.hernandez-garcia@mila.quebec)]
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