Artificial Neuronal Ensembles with Learned Context Dependent Gating
Authors: Matthew James Tilley
Michelle Miller
David Freedman
Venue: ICLR 2023 poster (2023)
Keywords: Continual Learning
Catastrophic Forgetting

Abstract: Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural networks suffer from catastrophic forgetting, where the network's performance rapidly deteriorates as tasks are learned sequentially. By contrast, sequential learning is possible for a range of biological organisms. We introduce Learned Context Dependent Gating (LXDG), a method to flexibly allocate and recall `artificial neuronal ensembles', using a particular network structure and a new set of regularization terms. Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. The gates are outputs of networks themselves, trained with a sigmoid output activation. The regularization terms we have introduced correspond to properties exhibited by biological neuronal ensembles. The first term penalizes low gate sparsity, ensuring that only a specified fraction of the network is used. The second term ensures that previously learned gates are recalled when the network is presented with input from previously learned tasks. Finally, there is a regularization term responsible for ensuring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones. We demonstrate the ability of this method to alleviate catastrophic forgetting on continual learning benchmarks. When the new regularization terms are included in the model along with Elastic Weight Consolidation (EWC) it achieves better performance on the benchmark `permuted MNIST' than with EWC alone. The benchmark `rotated MNIST' demonstrates how similar tasks recruit similar neurons to the artificial neuronal ensemble. 

Published as a conference paper at ICLR 2023
ARTIFICIAL NEURONAL ENSEMBLES WITH LEARNED CONTEXT DEPENDENT GATING
Matthew J. Tilley, Michelle Miller & David J. Freedman Department of Neurobiology, University of Chicago Chicago, II. 60637, USA (mitilley, memiller1, (erreddman@uuchiccae
ABSTRACT
Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing any- thing analogous to these neuronal ensembles. Further, artificial neural networks suffer from catastrophic forgetting, where the network's performance rapidly de- teriorates tasks are learned sequentially. By contrast, sequential learning possible for a range of biological organisms. We introduce Learned Context De- pendent Gating (LXDG), method flexibly allocate and recall 'artificial neu- ronal ensembles', using a particular network structure and new set of regular- ization terms. Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. The gates are outputs of networks themselves, trained with sigmoid output activation. The regularization terms have introduced correspond to properties exhibited by biological neu- ronal ensembles. The first term penalizes low gate sparsity, ensuring that only specified fraction of the network is used. The second term ensures that previ- ously learned gates are recalled when the network presented with input from previously learned tasks. Finally, there a regularization term responsible for en- suring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones. We demonstrate the ability of this method to alleviate catas- trophic forgetting on continual learning benchmarks. When the new regularization terms are included in the model along with Elastic Weight Consolidation (EWC) it achieves better performance on the benchmark 'permuted MNIST" than with EWC alone. The benchmark 'rotated MNIST" demonstrates how similar tasks recruit similar neurons to the artificial neuronal ensemble.
INTRODUCTION
1.1 CATASTROPHIC FORGETTING
Learning sequentially without forgetting prior tasks is commonly known as continual learning or life-long learning. When an artificial neural network is trained on task and the same model is then trained on a new task, the model's performance on the initial task tends to drop significantly. The model tends to overwrite parameters that are important prior learned tasks, leading to the well known problem referred to 'catastrophic forgetting'. : This phenomenon, initially coined as "catastrophic interference'. was first observed in 1989 (McCloskey & Cohen 1989) and longstanding problem encountered when training artificial neural networks. This problem is related to the mability-plastice dilemma (Mermillod et al., 2013). The dilemma arises due the trade off between plasticity when changing parameters for tasks and stability when keeping them the same for prior learned tasks.
The brain is not susceptible to such problems and it is able to adeptly learn many tasks. For ex- ample, whilst humans are still quite capable of forgetting, they are able to learn the rules of chess, football. cricket, poker, and many other games sequentially, without forgetting the rules to the others. The mechanisms how the brain does this are not fully known, though neuroscience experiments examining neuronal activity and plasticity in biological neural networks providing insights.

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One way in which the brain may enable continual learning could be by restricting plasticity of the synaptic connections that are important for a memory once it is formed. Upon the introduction of a new stimulus task, new excitatory connections are made on dendritic spines in the brain (Bonilla- Quintana & Wörgötter 2020] Makino & Malinow] 2009). As learning occurs the strength of these connections increases via long-term potentiation, where the response of the synapse for a given stim- ulus occurs more readily (Alloway) 2001] Yuste& & Bonhoefferr (001). Furthermore, the connections which were important for prior tasks experience reduced plasticity, thereby protecting these connec- tions from being lost as new tasks are learned (Bliss & Collingrigee 1993] Honkura et al., 2008) This property of biological networks inspired the introduction of Synaptic Intelligence (Zenke al., 2017) and Elastic Weight Consolidation (EWC) in artificial neural networks (Kirkpatrick et al., 2016), both of which have means of calculating the importance of individual weights to prior tasks. The regularization terms introduced in these methods encourage the network to find solutions that change the most important weights as little as possible.
Another proposed mechanism is the idea that memories are encoded in distinct 'neuronal ensem- bles', that is to say different sets of neurons are recruited to encode memories in different con- texts (Mau al. 2020). This use of neuronal ensembles may reduce interference between memo- ries and prevent the kind of catastrophic forgetting observed in artificial neural networks (González al. )
There are three broad categories of methods through which mitigating catastrophic forgetting has been implemented: replay-based methods (Li & Hoiem [016 van der Ven & Tolias 2018 Rebuffi iet al. (2016), regularization methods (Kirkpatrick et al. 2016 Zenke et | |017] Jung et al. (016)) and architectural methods (Schwarz et al.) 2018: et al. [0188] 8 Aljundi et al., 2016). The model we introduce in this paper uses a combination of architectural and regularization methods.
1.2 RELEVANT WORK
In 2017, method was introduced known as Context Dependent Gating (XDG) (Masse et al. 2018 and was demonstrated to successfully alleviate catastrophic forgetting in feedforward and recurrent architectures image recognition tasks like permuted MNIST and Imagenet. This method works by multiplying the activity of the hidden layers of an artificial neural network by masks or 'gates'. In XDG the gates are binary vectors where a random 20% of the nodes in the hidden layers of the network are allowed to be active for each context and activities for the remaining 80% of units are set zero. This allows the network use different sub-networks which have little overlapping representations (though it was allowed, at chance, overlap was roughly 4%). In combination with Elastic weight Consolidation (EWC) (Kirkpatrick al. 2017), the XDG method achieved strong performance on continual learning benchmarks. For XDG the gates applied for each task were randomly allocated and the context signal used to retrieve them had to be given as an additional input. In many realistic continual learning scenarios a context label is not specified and the allocation of random gates may not be optimal. The method introduced in this paper is an extension of XDG where gates are instead the outputs of trainable networks themselves.
1.3 OUR CONTRIBUTION
We introduce learned context dependent gating (LXDG), where the gating of the hidden layers is trainable and is dependent on the input of the tasks. Crucially, an advantage over XDG is that this method does not require any additional label specifying which task is being presented when per- forming on previously learned tasks. For example, if new data from a previously learned task is presented without the associated context label, would not be possible for XDG to perform well without finding the context label that was used to train that task. The is because XDG requires a con- text vector to reproduce the correct gating. Some methods have attempted to address this limitation of XDG, such as the Active Dendrites Network (Iyer et al. (022). In this case context vectors are still required, but they are derived from a simple statistical analysis of inputs. The LXDG method no longer requires a context vector to be explicitly provided or inferred. The gates for LXDG are learned and dynamically produced using a set of newly introduced regularization terms. The method is able to simultancously increase task performance and allocate the gates in single loss function. The random gating in XDG does allow for some level of overlap at chance. However, there is no flexibility for the model to potentially benefit from allowing overlapping learned representations for
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similar tasks. This provides a potential advantage for LXDG as the gates are not chosen at random, as they are XDG. Instead the loss function can encourage the inclusion of units that benefit task performance. Continual learning is demonstrated with this method on the permuted MNIST (Good- fellow et al.] 2013) and rotated MNIST benchmark tasks. There are three additional regularization terms required for the method to allocate new sparse gates for new tasks and recall prior gates for previously learned tasks.
2
METHODS
We trained the models on two benchmark tasks, permuted MNIST (Goodfellow et al. 2013) and ro- tated MNIST. Permuted MNIST is task in which the pixels of the traditional MNIST images (Lecun) ct al. 1998) are shuffled to create new tasks. Rotated MNIST tasks were generated by rotating the MNIST images by random angle, sampled uniformly between 0 and For the training and test set in each task, there are 60,000 and 10,000 images respectively. The model was trained on 28x28 per- muted MNIST images across 50 tasks (i.e. 50 unique ways of shuffling or rotating the image), each with 20 epochs. The models are trained with backpropagation with a learning rate of .001 along with batch sizes of 256. All models were trained in Pytorch (Paszke et 2019) on NVIDIA's GeForce GTX 1080 Tis.
The architecture of the model feedforward and shown in figure T includes two fully connected 'forward' hidden layers each followed with a ReLU output activation. Dropout (Srivastava et al.) 2014) was included after each layer with probability of 0.5. There are adjacent gating layers whose output are a multiplier for the output of the 'forward' hidden layers. Each of the forward layers has 2000 units. There is a final output layer for the ten classes. In the gating layers, there are two fully connected layers with 400 units for each of the 'forward' layers. These layers also use a ReLU output activation and are followed by a dropout probability of .5. The output of the gating layers uses sigmoid output activation. For consistency in the number of trainable parameters, all comparisons are made with an identical network structure, even if the gate regularization terms were not included during training.
2.1 REGULARIZATION TERMS
The gating layers require their own regularization terms to perform correctly. We are using the layers approximate a function that takes the inputs for the task and outputs different sparse gates for each task. Let's 's call the function we are trying to approximate The function g should take input data d and output the correct gating u for each context,
layers ge(d) = us:
the resulting vector u; is used to multiply the activities of each hidden layer
The method must do three things, allocate a sparse gating for each task, allocate new gating for new tasks and retain stable gates when presented with previously learned tasks. Each of these properties can be expressed loss function for the network.
Sparsity Loss: Ideally we want uy to be a sparse binary vector, for gate sparsity we can define a loss function by
Csparee E1 (9ild) (x(d) 3.12.
(2)
Nhia
where Bg is the sparsity parameter set by default to 3, 0.2. Choosing the correct B, is a compro- mise between optimizing the capacity of the subnetworks and ensuring the overlap of the networks does not cause overwriting of past gates. We kept the ;9, 0.2 in accordance with the XDG paper. produced with sigmoid output
This sparsity loss is only expected to work because the gates activation. We find that the artificial neural network tends arrive at a solution where the gate outputs associated with 20% of the nodes have value 1 and the rest have value Rs O. Ensuring the out- puts of the gates are as close as possible to binary vectors is important for the artificial network to produce something similar to 'neuronal ensembles'.
Change Loss: For allocating new gates for new tasks the prior input data for task f, d', can be used to ensure that new gates are as orthogonal as possible from the previously learned gates. These gating
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Gate Vectors, Sigmoid Output Activation
Input
Output
Gating Layers
Figure 1: Schematic demonstrating a simplified structure of LXDG, The gating layers high- lighted in green. The black dots indicate gate output values of ss 0. Input is an example permuted MNIST image.
functions fi take random sample of data from previously learned tasks d' to reproduce prior gates. For perfectly stable gating functions the vectors (id')) are identical to the vectors g (d'), where of are the gating functions after training on prior task t. The regularization term that encourages the production of new gates can then be defined as
Cohangg E) gild") 9 99(dd)
Bel²
Nuidd
where each prior task has an associated sample of unlabeled input data d'. To ensure new gates are as orthogonal as possible from all prior gates the change parameter can be set to 9g If were to set 8e 0, this would specify a desired overlap with prior learned gates.
Keep Loss: For maintaining the stability of gates associated with prior tasks, final loss can be constructed,
Cheep Σ ge (d() g.(d") 3
Niada
The new gating function g should produce the same gates the prior ones (d'), when presented with input from prior tasks d'. In this loss function 3h > 33 to ensure that the new gating function reproduces the vector when presented with inputs from previously learned tasks. By default 8% was chosen to be 0.3 as the only requirement for this parameter is that it should be greater than 0.2. This encourages the term (dd) (dd)) to maximized up to 0.3. However, as the sparsity should not be greater than 0.2, the term should ideally not reach that maximum. This method does require batch of input data from prior tasks d², but it does not require the labels from previously learned tasks.
Lastly, the Elastic Weight Consolidation (EWC) regularization term (Kirkpatrick et al. 2017 included. The term comes in the form,
C'wwe 2
F' -)2
(5)
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for each prior task t, where F is the diagonal of the Fisher information matrix, l the model parameters at the end of training task and are the most recent network parameters. The diagonal matrix of parameter 'importance' and the
of the Fisher information matrix can be interpreted constraint works by preventing important weights from changing too much during training.
The total loss function then becomes,
C = Ltask + Aspar Sipprrse
Σ Dehhangeechange + >keep + NEW NEwcLewch,
(6)
where three of the losses are included a sum over prior learned tasks t. The coefficients for each of the losses are Asparses Achanges Akeep. and λEWO- Here Lrask is the task loss, minimized for best performance. If the method is to be used for reinforcement learning minimizing Ctask could maximize future returns instead.
The desired behavior of the gating layers is produced when these regularization terms are simulta- neously minimized. The regularization terms allow the network to allocate new sparse gates for new tasks and recall learned gates when presented with inputs from previously seen tasks.
2.2 PARAMETER OPTIMIZATION
The coefficients Asparses Achanges Akeep are set to the same values, Agates for simplicity in the optimization. The two parameters in the loss function λgwC and Agate were optimized with sweep across a range of values. We anticipate that will be possible with future work to perform full grid search and to include the different À parameters for each of the introduced regularization terms. However, due to computational constraints, we performed search by first finding the optimal Agwe parameter and then sweeping across values for Agate-
The coefficient for the EWC regularization was first optimized using a scan over values 0.1, 0.2, 0.5, 1, 5, 10, 20, 100, 200, 500, 1000, 2000, 5000 and 10000. The model was trained on the task permuted MNIST and the mean accuracy after 50 tasks was stored for each case. The optimal point was 1000 as shown in figure
Mean Accuracy Across 50 Tasks
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Figure 2: Mean performance across 50 permuted MNIST tasks when training with EWC alone. Scan across 15 values of λgwc. The optimal point was found at value of 1000, shown in red.
With the value of λewc fixed to 1000 we performed a scan for the gating coefficient, Agate- The values used were 0.5, 1, 100, 200, 500 and 1000 for this parameter sweep. The performance in- creases with Apate to limit of approximately 95% as shown in figure 3 We chose the optimal value of Agate 1000.
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Mean Accuracy Across 50 Tasks
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Figure 3: Mean performance across 50 permuted MNIST tasks when training with LXDG EWC. Scan across 15 values of the Agate Optimal point found at the maximum value of 1000, shown in red.
3 RESULTS
3.1 PERMUTED MNIST
We trained models on the permuted MNIST (Goodfellow et al., 2013) set of tasks. Each Permuted MNIST task is a unique shuffling of pixels for the MNIST images. As expected, figure Ishows that the model with no additional regularization terms in the loss catastrophically forgets. As the tasks are presented sequentially the mean accuracy across all learned tasks drops precipitously. For the models including EWC regularization the performance is much better than with no constraints on the loss. The final mean accuracy of EWC across 50 tasks is 72 + 1%(SD). The best performing model is XDG+EWC where the gates are random, unlearned and the context is given to the network directly. The performance of LXDG in figure is much closer to XDG+EWC than just EWC alone. The mean accuracy of LXDG+EWC across 50 tasks was 96% whereas for XDG+EWC mean accuracy
97%. We did not expect LXDG+EWC to outperform XDG+EWC due to the significant advante being given the context label directly. There appears to small instability for the very first set of tasks trained with LXDG+EWC. However, after tasks the performance remains stable.
To analyze the structure of the gates and how they form task are introduced we performed princi- ple component analysis (PCA) on the set of learned gates. The mean output of the gates for the final model tested on all 50 tasks was saved. Then PCA was used to obtain the two principle components. The separation of the gates can then be displayed in figure 5) As we would expect unique gates are being found for the tasks. The first gate that was trained is indicated green for figure It appears to be quite different from the others and may explain the early drop in mean accuracy as the first task does have the lowest accuracy throughout training. The main difference for how this gate was trained when compared to all the others is that at the start of training the network weights are initial- ized by sampling over a uniform distribution. Further analysis of the first gate shows that it does not choose a binary vector as the solution. See figure & in the appendix for the gate output. However, all subsequent gates appear to be allocated correctly with the expected binary vector- like behavior in the outputs. The sparsity in the network weights after training the first permuted MNIST task may be important for producing the correct gate outputs. For layer 1, the first principle component (PC1) appears to separate the gates by when the associated task introduced.

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Figure 4: The mean accuracy on 50 permuted MNIST tasks for the LXDG EWC model (solid red), EWC alone (blue dashed), XDG EWC (black dashed) and no constraint (green dash-dotted). As the model sees each new task, its performance on the current and all prior tasks averaged. This is repeated for each task to produce the mean accuracy across tasks. The bands are +1 standard deviation mean accuracy with tasks produced using 3 different random seeds for EWC only and LXDG EWC. The orange dotted line indicates chance level performance.
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Figure 5: Principle component analysis of recalled gates when the LXDG+EWC model is trained on 50 tasks of permuted MNIST. The gates were analyzed as binary vectors where the gate output was considered 'active" if the mean output of the gate order that the tasks were presented, from blue to red. The exception is the gate associated with the first task, presented in the color green. The first gate appears be anomalous and may explain the slight instability in the initial performance observed in figure 4
greater than 0.1. The points are colored in the

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3.2 ROTATED MNIST
The models were trained on an additional set of tasks, rotated MNIST. Each task was generated by rotating the MNIST images by random uniformly sampled angle between 0 and æ. The advantage of using this benchmark is that similarity between tasks is easily quantifiable. The performance of the method on rotated MNIST above EWC alone demonstrated in figure 6 More interestingly for this benchmark, analysis of the produced gates demonstrates that similar tasks produce similar gates. This provides the potential for knowledge of prior learned tasks transfer new ones by producing similar gates. In the loss function, the target is still for the gates to be orthogonal as possible, 3. = However, to satisfy the other parts of the loss function the network chooses compromised solution, where the gates for similar tasks are less than orthogonal.
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The model with LXDG+EWC (red, solid) remains more consistent with higher performance throughout the 50 tasks of rotational MNIST than the model with EWC alone (blue, dotted). The EWC only model and the model with no additional regularization (green, dash-dotted) begins regain some level of performance later on as it is shown tasks with similar rotations to those previously learned, but does so in less stable manner than the LXDG+EWC model. The bands are #1 standard deviation mean accuracy with tasks produced using 3 different random seeds for EWC only and LXDG+EWC.
Figure 6: The mean accuracy across 50 tasks of rotational MNIST.
To investigate if the task structure is reflected in the structure of the gates, a principle component analysis was performed on the recalled gate outputs. In figure the gates form a ring structure and the points on the ring are related to the angle that was used produce the task. Similar tasks with similar rotation angles produce similar gate outputs.
4 CONCLUSION
This work demonstrates the ability of LXDD to alleviate catastrophic forgetting for a set bench- mark tasks. Unlike prior work, XDG (Masse et al. 20188), this method of allocating gates to alleviate catastrophic forgetting does not require explicit context labels. Instead, the method is able to perform well previously learned tasks without specifying which task it was. It achieves this by learning to allocate the correct context gating from the task input alone. The LXDG+EWC method produced good performance two continual learning benchmarks, permuted MNIST and rotated MNIST.
The rotated MNIST benchmark tasks demonstrates how tasks presented with a similar rotation to prior learned task will produce similar gating. This presents the network the opportunity to
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Figure Principle component analysis of LXDG gate outputs across 50 tasks. The points represent the gate for each task of rotated MNIST. Unlike figure points are color-coded not in the order that the tasks were presented but by the rotation angle of the MNIST images blue (0) to red (x). Similar angles produce gates close together in the reduced 2 dimensional space. As in figure the gates were analyzed as binary vectors where the gate output was considered 'active" if the mean output of the gate greater than 0.1.
transfer the information stored in the weights from prior tasks to new similar tasks. This is because the starting place for the model learning new task will automatically produce gates for tasks similar to those seen previously. We hope that by building on this work, this method can be used to produce transfer learning capabilities in significantly more complicated tasks.
Because the method recalls gates from previously seen input data alone, without the use of the prior task labels, the method can used in a reinforcement learning (RL) context. Future work should explore the performance of the model on benchmark RL continual learning tasks, such as the Atari 2600 suite of games.
This method was inspired by the biological process of encoding newly acquired memories or task knowledge in neuronal ensembles. For this reason, a future direction of this work would be to use LXDG as a model to compare with how biological neural networks assign and recall neuronal ensembles. With neuroscience experiments performing recordings of neural activity in animals (e.g. monkeys and mice) it will be possible to observe these neuronal ensembles and assess overlap between neuronal ensemble for memories encoded in different contexts. In particular, it is possible to measure the active neurons in ensemble when mouse is performing task in context performing a similar but different task in another context (Mau et al.) 2020). It will be interesting investigate if biological networks assign ensembles for contexts in similar way to how the LXDG method assigns new gates. For example, we are interested in getting LXDG to reproduce the behavior biological networks where memories encoded closer in time share more overlap their neuronal ensembles (Cai et al. 2016).
Assigning and recalling neuronal ensembles could be an important step in producing artificial in- telligence that can interact with the world reliably, in a multitude of changing contexts. We hope that LXDG can be developed further help produce flexible artificial neural networks capable of continual learning in challenging environments.
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Published as a conference paper at ICLR 2023
A
APPENDIX
A.1
PERMUTED MNIST GATE PROPERTIES
Output of Gate O, Layer 1
Output of Gate 1, Layer
08
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0.6
0.
0.2
500
750
1000
1250
1500
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2000
Node
Node
Figure 8: Mean output of the first (left, red) and second (right, blue) gates. Demonstration of the problem with the initial gate. The first gate satisfies the sparsity loss but not by producing a binary vector the output. However, All other subsequent gates do appear to produce the expected behavior.
A.2 PERFORMANCE COMPARISON
The recently published model referred to as active dendrites (Iyer et al. 2022) has been added to the performance plot in figure In this model context vector still required at train and test time. However, the model includes a method to get the context vector from simple statistical analysis of the inputs. Therefore, while the method differs substantially from LXDG, which does not a context vector at all, it is an interesting point comparison. We have taken the values directly from the paper, indicated by the asterisk in the figure. We would have preferred reproduce the results ourselves but given time constraints, this was not possible. For this reason we do not want overstate our confidence in the comparison. The active dendrites model combined with the similar synaptic intelligence (Zenke et al. 2017) (SI) method instead of EWC. We find SI often marginally outperforms EWC so this should give active dendrites slight advantage in this compar- ison. However, with this naive comparison LXDG does outperform the active dendrites method in mean performance 50 tasks. Conveniently, the model sizes are comparable, active dendrites used two layers of size 2800 and LXDG uses two forward layers of size 2000 and two hidden layers of size 400 for each the hidden layers. The final mean performance at 50 tasks for LXDG+EWC was 96.0% and active dendrites has mean performance of 94.4%.
12

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Published as a conference paper at ICLR 2023
0.8
Mean Accuracy
0.6
EWC, learned context
EWC only
XDG EWC, hand input context
No constraint
0.4
Active Dendrites Network SI
0.2
Permuted MNIST task
Figure 9: Performance comparison of LXDG + EWC (red solid), EWC only (blue dotted), XDG EWC (black dashed), no constraint (green dash dot) and active dendrites SI (purple solid). The asterisk indicates that the values are taken directly from the paper (Iyer et al. 2022) and not reproduced by ourselves.
13


Reviews:
[{"review_id": "ppuAdPUMcn", "reviewer": "Reviewer_6wb8", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper extends the context-dependent gating (XDG) approach to avoid catastrophic forgetting in life-long learning, i.e. learning a new task might overwrite the weights learned for another task. XDG randomly disabled 20% of the neurons in each layer to ensure the creation of distinct neural ensembles for different tasks. This paper proposes to learn these input-dependent gating masks using a neural network. The authors showed that such a network yields superior performance for solving a  continual learning benchmark such as permuted MNIST and rotated MNIST in object recognition. ", "summary_of_the_review": "The paper proposes an extension of the XDG approach to alleviate catastrophic forgetting in life-long learning, with compelling results.  Using a neural network to learn the context dependent gating is new in this context. However, the algorithm has not been rigorously compared with other state of the art algorithms and against more challenging benchmark in  the field of continual learning.  It has promise but the  technical innovation and implications currently are quite limited. ", "strength_and_weaknesses": "Strengths: The paper addresses an important problem, and the results represent a significant advance relative to Masses, Grant and Freedman's PNAS paper.  The results, relative to EWC and no constraint are compelling. \nWeakness: It is not clear this is the state of the art in continual learning, which is a very active area of research in ML, and has a leaderboard with many different tasks and many algorithms.  This paper does not address any of those tasks. The permuted MNIST and rotated MNIST might be too simple. ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "iLb4Isn9zC", "reviewer": "Reviewer_LEqE", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "Context-Dependent Gating (XDG), proposed by Masse, Grant, and Freedman, showed promise in dealing with catastrophic forgetting. In that work, gating is randomly chosen before training. This work follows up with the obvious next question: how to learn gating for different tasks. Authors show Learned Context Dependent Gating (LXDG), combined with Elastic Weight Consolidation (EWC), outperforms existing methods on permuted MNIST and rotated MNIST datasets. ", "summary_of_the_review": "This work is a natural extension of XDG, well executed.", "strength_and_weaknesses": "Strengths:\nThe paper systematically compares the new method to alternatives and provides clear evidence of superiority.\n\nWeaknesses:\nI see no significant weakness.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "X-mMEwF0KK", "reviewer": "Reviewer_wV5E", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper addresses the problem of catastrophic forgetting in deep neural networks by introducing a complementary network for gating the neurons. This second network is trained based on the same input to produce task-aware gates which are sparse and regularized to be similar within each task while orthogonal between the tasks. The authors test their architecture on permuted- and rotated-MNIST tasks and show that their model, with no supplied labels, exhibits a performance close to that of the existing models which required task labels.", "summary_of_the_review": "The paper adds a simple yet powerful tweak to the existing continual learning approaches allowing to omit task labels at the evaluation time. I believe that the model can be further explored to assess its other properties such as a potential increase in the number of tasks learnable with a single network. Such properties – if shown – may strengthen the paper.", "strength_and_weaknesses": "This work contributes to the field of continual learning by introducing a context-aware mechanism of gating the network neurons while learning.\n\nAmong the strengths of the work are:\n\n-A nice and simple model that learns to identify the tasks to for the gates (I am surprised that it hasn’t been done yet!)\n\n-High performance, matching that of the previous approaches, but with no need to supply task labels\n\n-I enjoyed the analysis which shows what similar tasks (i.e. MNIST data rotated to small angles) lead to similar gates.\n\nWith that said, I believe that additional analysis could showcase the abilities of this model and thus strengthen this contribution. It looks like the content-aware gating which generates similar gates for similar tasks should be more parsimonious than the existing approaches and thus should be able to pack more tasks in the same neural network. Can the Authors please comment on that proposition and/or run such an experiment? It shouldn’t take long.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "01K-bNku104", "reviewer": "Reviewer_jDUA", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper develops a method of adding gates to hidden units in order to protect their weights from modification during training on new tasks. The goal is to prevent catastrophic forgetting. The method, LXDG, extends a prior method XDG, by (among other things) removing the need for a task label.", "summary_of_the_review": "I loved this paper. Well-written, interesting, well-scoped, with convincing and valuable findings.\n", "strength_and_weaknesses": "\n(strengths)\n\nThe background context is clear.  The paper is well-scoped, and the paper delivers incremental but significant improvement on prior work. \n \nThe findings are convincing.\n\nThe work has significant value. \n\nMiscellaneous comments:\n\nPunctuation errors: 2 commas near top of page 2, period in first line of page 5.\n\nPlease number equations.\n\nRe lambda_gate: better might be to give different lambdas in the equations, then in the first paragraph of 2.2 note that they were all set equal. Alternately (and more standard I think), withhold the lambdas from the individual loss equations, and include them in the collected Loss eqn at top of pg 5.\n\nI did not get the last paragraph of 3.1. Is this a meaningful observation? ie, is the network in the paper similar enough to bio networks to draw any conclusions?\n\nIn section 3.2: Did the value of beta_c (in L_change) change since the tasks were so closely related?\n\nFig 5 and 7: please specify the task type (permuted or rotated) in the caption title.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster