Summary and Contributions: This paper revisits the direct feedback alignment algorithm, benchmarking it on a wider variety of datasets than had been before. The author finds that, despite previous results suggesting that DFA scales poorly to difficult image classification problems, it works well on a variety of other tasks that had not been considered before.
Strengths: This is a paper focused on empirical results, aand the experiments are extensive and thorough, with appropriate attention given to the need for using different hyperparameters for backprop and DFA.
Weaknesses: 1. The choice of tasks in the paper is (to this reviewer) feels arbitrary, particularly neural view synthesis and click-through rate prediction. Why did the authors choose these tasks and not others? Were the authors motivated by applications, or did they choose tasks where they thought DFA had a good chance of performing well? If so, what made these tasks seem promising? Can the authors suggests some tasks, besides image classification with ConvNets, where they would not expect DFA to perform well? 2. Related to the above, I think readers of this paper will likely wonder the following: why does DFA work on some tasks and not others? Is task or architecture the more important factor? What are the minimal changes to the problem or architecture that could "break" DFA's performance on the tasks where it works well? Could these provide insights into how to rescue DFA's performance on tasks where it has fared poorly, like image classification? 3. On the NLP task, which seem like the most "standard" task the authors tried, DFA performance lags substantially behind backprop performance. I feel this is not sufficiently emphasized in the paper. The abstract, for example, gives readers the impression that DFA works at near-backprop levels on this task, just like it does with the others.
Correctness: The empirical evaluations seem rigorously done. The claims are mostly supported by the experiments, but I feel like the current presentation is misleading in two ways. 1. As mentioned above, the NLP results are not as good as suggested by the abstract and introduction. 2. I think the title of the paper is a bit misleading. Are these tasks really a representative sample of "Modern Deep Learning Tasks and Architectures?" When I had only seen the title of this paper, I, for instance, assumed that it was going to show a way to rescue DFA performance on ImageNet. Given that it does not, and that the NLP results are not as strong as the rest, I think this paper is less about refuting the claim that DFA doesn't scale and more about showing that the situation is complicated and more study is needed. I think a different paper title could convey this impression more accurately.
Clarity: Yes, it is clearly written.
Relation to Prior Work: The work is well-motivated in the context of both previous biologically plausible learning papers and more practically minded algorithms intended to help deep learning scale better. I think more discussion of the practical implications of DFA relative to the other methods discussed would be beneficial.
Additional Feedback: I think the empirical results are important and show that it is worth making a good-faith effort to scale feedback alignment-related algorithms. I would likely be willing to update my score if some of the issues I've mentioned are addressed. *************** Update: the authors have addressed several of my concerns in the response and I am raising my score accordingly. I do think acknowledging the less impressive results on the NLP task in the abstract is important (in fact, it is an interesting finding!)
Summary and Contributions: The authors present a study that uses direct feedback alignment (DFA) to train various models on various challenging tasks. The work is motivated by arguing that DFA was so far only used on small datasets, and was shown to not perform well on computer vision tasks, in part because of the usage of CNNs in these settings. This survey challenges these views by conducting an extensive set of experiments using DFA to train s.o.t.a. models on s.o.t.a. benchmarks. The benchmarks include view synthesis, language modeling, recommender systems, and graph embedding. They compare the performance of these models to ones trained using a normal BP approach. The authors show that DFA can be competitive to classical BP in many scenarios, and also show how further improvements could be implemented. They also highlight potential benefits (e.g. parallelization) of training models with DFA vs BP. ****** Update: The author's response covers my comments and I will keep my positive score.
Strengths: The main strengths are: - Extensive set of experiments that reassess DFA - Diverse choice of s.o.t.a. benchmarks for DFA evaluation - Selecting state-of-the-art settings and benchmarks instead of the often used simple (toy) datasets. Each benchmark section has a concise set of background information, description of the setting and presentation and interpretation of the results. This makes it very clear to the reader, where DFA is competitive, and where further investigation is needed (e.g. in the NLP task).
Weaknesses: The main weaknesses are: Though the results shine a new light on DFA, the method and how it is used is not a novelty by itself, hence the study does not present a novel method or a new variation of an existing method. it 'only' applies (though very extensively) the DFA training method to existing models and existing datasets.
Correctness: The claims and the stated contributions to the field are correct. The empirical methodology and procedures (incl. grid search of hyperparameters etc.) are correct as well.
Clarity: The clarity of the paper is good. It is well structured and the different benchmarks and settings are properly explained. Most questions that came to my mind while reading were either covered directly or by the appendix material. Only critique here: some abbreviations are not introduced.
Relation to Prior Work: The authors explain how DFA was used before, what the scope of the previous experiments and their limitations were. The authors make good use of the extended bibliography and cite all the relevant previous contributions.
Additional Feedback: Make sure you introduce all abbreviations.
Summary and Contributions: This paper provides an extensive empirical evaluation of direct feedback alignment (DFA), a simple and scalable credit assignment algorithm. Unlike backpropagation of error (BP), DFA does not require weight transport and it does not need symmetric backward connectivity. Experiments are conducted on deep neural network models for neural view synthesis, recommender systems, geometric learning, and natural language processing. The experimental evidence is convincing. On the tasks considered DFA does not always match BP, but it produces useful weight updates. ** Update: I maintain my positive score after reading the authors' response.
Strengths: - Focusing on DFA was an excellent choice. Unlike feedback alignment, DFA does not require symmetric connectivity. This makes DFA a very appealing model for neuroscientists, and a useful algorithm for hardware designers (perhaps even for distributed software implementations). The feedback architecture of DFA is also a natural first choice for synthetic gradient modules (see, e.g., Lansdell et al., ICLR2020). - Large-scale experiments that involve a number of different architectural elements. - Appropriate controls.
Weaknesses: - Being a purely empirical paper, which studies an existing method, there is no theoretical or algorithmic novelty.
Correctness: - It would be good to always very clearly note in the main text whenever weight transport is violated when training with DFA (e.g., attention layers as discussed in the SM). - Eq. 1 (typo?): shouldn't $i$ start at the first layer, up to layer $N$?
Clarity: The paper is well written and clear, with sufficient detail, well positioned within the literature. I imagine it is some work, but it would be useful for the reader unfamiliar with all the architectures involved -- like me -- to provide (simple) network diagrams in the SM.
Relation to Prior Work: The paper is well-positioned in the literature. Some ideas to further strengthen the discussion section: - It could be good to cite (1), which provides more evidence that DFA has troubles in training convolutional layers in image classification tasks, and contains some surprising findings on DFA with sparse feedback. - The results could be seen as a promising starting point, which encourage improving the simple DFA baseline with a learning to learn algorithm, see e.g. (2). Personally, I find this point of view exciting.
Summary and Contributions: The paper applies an existing algorithm called Direct Feedback Alignment (DFA) to diverse tasks and datasets, largely beyond what prior work has done experimentally with DFA. DFA is an alternative to the conventional backpropagation algorithm. The authors point out two benefits of DFA over backpropagation: 1/ DFA allows to compute the gradients of all the weights in parallel and update them synchronously, rather than successively (computational considerations). 2/ DFA does not suffer from the biologically implausible weight transport problem of backpropagation (biological considerations). The paper establishes a surprising result, that learning under synaptic asymmetry is possible beyond fully-connected layers, across a variety of tasks.
Strengths: The authors conduct an extensive study demonstrating the ability of DFA to train modern DL architectures, across a variety of tasks: neural view synthesis with neural radiance fields, click-through rate prediction recommender systems, geometric learning with graph convolutional networks, and natural language processing with transformers. This work should be useful to establish baselines for other biologically plausible learning algorithms in the future.
Weaknesses: One of the claims of the paper is that DFA can help reduce training time as well as power consumption if implemented correctly. This claim is made in the abstract, introduction, conclusion, as well as in the broader impact section. Since this claim is made in many places of the paper and used as a central argument for studying DFA, it would be helpful to have a more detailed explanation, with quantitative arguments if possible, of what would be the implications of using DFA rather than backpropagation, and what would the challenges to be overcome. With an appropriate implementation on GPUs, what are the expected gains? Denote N the number of processing stages (say N layers if we consider a standard multi layer neural net). 1/ The time required with backpropagation is: - N in the forward pass, - N in the backward pass, - plus the time required for all weight updates. 2/ One can argue that, if implemented correctly, the time required with DFA is - N in the forward pass, - 1 in the “backward pass” (all “gradients” are send through direct feedback connections in parallel), - plus the time required for all weight updates. So the overall time cut-off seems to be bounded by a factor 2. Or am I missing something? With a neuromorphic implementation, I can imagine that one would get significantly more speedup, but there seems to be many other problems to be overcome, like computing the derivatives of the forward activations (denoted f’), or the fact that you are still using backprop in the attention mechanism (as transparently explained in appendix D). These concerns should be addressed, too.
Correctness: DFA is presented in section 2. The presentation is limited to the setting of a vanilla multi-layer neural net (no skip-layer connection, no convolution, no attention mechanism, …, nothing fancy). However, in experiments, DFA is used in architectures much more complicated than this simple setting, with convolutional nets and transformers in particular. More details seem necessary to explain how this is done in these settings. I can see that appendix D briefly explains how DFA is adapted to attention mechanisms ; unfortunately, the reader has to search in the appendices by themself to find this piece of information. How does DFA adapt to other settings? Can this method be applied with any computational graph or are there limitations?
Clarity: The paper is relatively well written. It is a bit repetitive in places though.
Relation to Prior Work: The relation to prior works, which seek either for biologically plausibility or efficient use of compute resources, is well explained. Whereas previous work has applied DFA to (mostly) computer vision tasks, achieving disappointing results on challenging datasets, the authors consider here very different tasks, datasets and architectures.
Additional Feedback: How are the (random) feedback weight matrices B_i (which serve as random projections) initialized? Line 263. It would be good to define beta_2. In Eq 3 you use the derivative of the activation f’ to compute the “gradients”. Is f’ necessary? If yes, it would be good to explain why. In particular, have you experimented with different activation functions or only ReLU? With ReLU, f’ is either 0 or 1, meaning that for each weight, if the postsynaptic neuron is zero then it receives zero gradient, otherwise it receives a nonzero gradient. I would expect that this is actually a critical aspect of why the method works at all. I would consider raising my score if the authors address my === After rebuttal === Thank you for answering my questions! Congrats on the nice work! I understand that with DFA we could potentially very efficiently parallelize the backward pass and thus have a speedup (Note that this speedup is unlikely more than a factor 2, since we still need to do the forward pass sequentially). It is not really clear that power consumption can be reduced though.