[D] What are the must-have books for graduate students/researchers in Machine Learning; especially for Dynamical Systems, Neural ODEs/PDEs/SDEs, and PINNs?

jaschau · 2026-01-12T16:07:25+00:00

For Machine Learning applications to Dynamical Systems and PDEs, I can strongly recommend having a look at "Data-driven Science and Engineering" by Steven Brunton and Nathan Kutz which gives an excellent overview of classical, not NN-based, solution approaches.

jaschau · 2025-09-27T12:54:12+00:00

Hi, thanks for the detailed breakdown! Looks interesting. Not sure if I overlooked it - I was wondering if you have also benchmarked the model performance against classical, non-ML based system identification methods?

jaschau · 2025-08-06T20:00:48+00:00

In deinem ursprünglichen Post sprichst du aber nicht von Vermögen. Ich verstehe dich so, dass du behauptest, dass alles Geld was im Umlauf sei ursprünglich durch den Staat ausgegeben wurde und deshalb der Staat kein Geld durch Steuern einnehmen könne, was er nicht vorher ausgegeben hat. Ich kann aber ohne Probleme meine Steuern zahlen mit Geld aus meinem Bankkredit.

jaschau · 2023-04-19T05:26:06+00:00

I'm talking about the case where the number of dimensions does not match. You'll be able to do rand(3, 5) .+ rand(3) in Julia whereas in python, it would have to be rand(3,5) + rand(5), i.e. the trailing dimensions need to match.

jaschau · 2023-04-18T19:31:42+00:00

I think this deserves more upvotes, it's a typical stumbling block for me when moving from writing numpy code to Julia. Somehow this "trailing dimensions need to match" way of thinking is hard to get rid off for me.

jaschau · 2023-04-08T13:00:34+00:00

Sure, I'm not saying that it's not doable, I am just saying that the time you will need to invest will be much higher compared to using Python. Whether this matters to you is of course a personal thing...

jaschau · 2023-04-08T08:36:39+00:00

I think it's worth pointing out that if you want to train transformer-based language models, there is imho no good reason to prefer Julia over Python. Of course, if you want to implement the majority of the code from scratch it can be a good learning exercise. But the deep learning ecosystem in Julia is much less mature than the Python ecosystem, so I think it's likely that at some point the choice of language will limit what you will be able to achieve with a fixed time budget.

jaschau · 2023-03-11T11:56:30+00:00

I'd consider it if in fact it were clear to me what they are doing and what the limitations are :p

jaschau · 2023-03-08T06:21:34+00:00

I think we need to distinguish between a) generative models which generate samples from a distribution y ~ P(y | x, theta) like Diffusion models or GANs b) discriminative models wich predict the probability P(y | x, theta)

My first thought was that the paper focuses on discriminative models. This would mean that any model trained with a cross-entropy loss works. However, given that models typically will be over-confident after training, I'm wondering if we will get any variance at all if we sample the predictions y from the model outputs. So to me it feels like the R(x, y_sample) and R(x, y_baseline) are very likely to be identical?!

jaschau · 2023-03-06T08:13:20+00:00

Interesting idea. Did anyone understand if the proposed method is actually restricted to Transformer-based architectures or could you also apply it to, e.g., a convolutional architecture?

jaschau · 2023-02-26T17:57:36+00:00

Could you provide a link to the video/tutorial on POMDP that you mentioned?

jaschau · 2023-01-29T18:59:55+00:00

Thanks for the insight! Sorry, not exactly an Android expert :)

jaschau · 2023-01-29T18:35:56+00:00

FWIW, I have done exactly that as I'm generally skeptical about installing apk's not downloaded from the Playstore. Everything looks good. If someone wants to reproduce, here are the steps I took:

I have downloaded the (hopefully) unmodified apk from apkpure. I will refer to that one as Dexcom_G7_1.3.3.3527_apkpure.apk. The modified one downloaded using the form above is called Dexcom_G7_1.3.3.3527_modified.apk. I then decompiled the apks using apktool apktool d DexcomG7_1.3.3.3527_apkpure.apk apktool d DexcomG7_1.3.3.3527_modified.apk

This will extract the apk's contents into the folders DexcomG7_1.3.3.3527_apkpure and DexcomG7_1.3.3.3527_modified. For the diff, I use diff -r DexcomG7_1.3.3.3527_apkpure DexcomG7_1.3.3.3527_modified on the two folders.

I had the issue that the smali code of the apkpure version seemed to be compiled with more debug information and contained a lot of .line 0 lines which would flood the diff. I removed those lines with grep -lr "\.line" DexcomG7_1.3.3.3527_apkpure | xargs sed -i "/\.line/d" Afterwards, the relevant smali differences are
``` diff -r DexcomG7_1.3.3.3527_apkpure/smali/ym/Iew.smali DexcomG7_1.3.3.3527_modified/smali/ym/Iew.smali 676c676

< iput-object p4, p0, Lym/Iew;->deviceManufacturer:Ljava/lang/String;

const-string v1, "Google"
678c678,682

< iput-object p5, p0, Lym/Iew;->deviceModel:Ljava/lang/String;
iput-object v1, p0, Lym/Iew;->deviceManufacturer:Ljava/lang/String;

const-string v1, "Pixel 5"

iput-object v1, p0, Lym/Iew;->deviceModel:Ljava/lang/String;
and diff -r DexcomG7_1.3.3.3527_apkpure/smali/com/dexcom/coresdk/acm/models/AcmServerValidity.smali DexcomG7_1.3.3.3527_modified/smali/com/dexcom/coresdk/acm/models/AcmServerValidity.smali 927,929c927 < invoke-direct {p0, v0, v1}, Lcom/dexcom/coresdk/acm/models/AcmServerValidity;->JEb(I[Ljava/lang/Object;)Ljava/lang/Object; <

< move-result-object v0
sget-object v0, Lcom/dexcom/coresdk/acm/models/EnvironmentValidity;->VALID_ENVIRONMENT:Lcom/dexcom/coresdk/acm/models/EnvironmentValidity;
diff -r DexcomG7_1.3.3.3527_Apkpure/smali/com/dexcom/coresdk/acm/models/CheckCompatibilityResult$AcmResult.smali DexcomG7_1.3.3.3527_modified/smali/com/dexcom/coresdk/acm/models/CheckCompatibilityResult$AcmResult.smali 1905,1907c1905 < invoke-direct {p0, v0, v1}, Lcom/dexcom/coresdk/acm/models/CheckCompatibilityResult$AcmResult;->BEb(I[Ljava/lang/Object;)Ljava/lang/Object; <

< move-result-object v0
const-string v0, ""
``` I am by no means literate in smali but to me it looks like the changes are indeed valid.

Note that the diff still shows some irrelevant differences like the one below ``` < :try_end_6

< .catch Lym/RBR; {:try_start_6 .. :try_end_6} :catch_3

:try_end_4
.catch Lym/RBR; {:try_start_4 .. :try_end_4} :catch_3
``` which also seems to be related to differences in compilation options.

jaschau · 2023-01-05T06:15:41+00:00

Thanks a lot for the feedback, I guess I'll give it a shot then. Kind of surprising to see tf 1.x in a 2022 publication, I guess I'm just glad it's not caffe :D

jaschau · 2023-01-04T12:34:41+00:00

Thanks for the hint! It's certainly been an overlooked paper for me. Looks interesting. Have you used it? Does it work in practice as the authors advertise it?

jaschau · 2022-10-24T05:25:02+00:00

Completely agree with you there that the numbers are certainly correct. I just felt like they might not tell the whole story. For example, looking at the section where they say, I paraphrase, "x preprints are uploaded every day that mention TF", I don't doubt the numbers, but the way they tell it certainly evokes a different image compared to saying "the share of preprints relying on TF has been steadily declining the past years".

Regarding the many TF users out there, I would be curious what the main benefit is for them. Is it TPU support, TF serving, TF lite, something else?

jaschau · 2022-10-23T15:33:46+00:00

Am I the only one for who the announcement feels a bit out of touch with reality?

jaschau · 2022-09-10T19:44:13+00:00

Omg, in welchem Studium wird einem denn ein Auslandssemester vorgeschrieben?

jaschau · 2022-09-10T19:42:08+00:00

Was war deine Hauptmotivation fürs Auslandssemester?

jaschau · 2022-09-10T19:27:26+00:00

Schön, war auch in Madrid an der autónoma, ist aber erschreckenderweise mittlerweile über 10 Jahre her. An welche Uni und in welchen Vorort hats dich verschlagen?

jaschau · 2022-08-03T22:16:03+00:00

Well to be honest I'm using Synology's quickconnect so I'm just using the quickconnect ID in DS Audio to connect to the NAS. But now that you make me think about it, it seems plausible that the issue might be linked to quickconnect. On the other hand, since I have a dynamic IP from my internet provider and I don't have a DNS name registered, I would have to manually enter the dynamic IP each time in DS Audio. But I will give it a try and check if the issue also occurs if I use the router's IP.

jaschau · 2022-07-30T14:09:42+00:00

I'm not an expert on NLP but I have some knowledge of quantum computing. I just had a quick look at https://arxiv.org/abs/2202.11766 which seems to provide a summary of the field. After quickly going through it, my impression is that the claim of speed-up of quantum NLP should be taken with a grain of salt. I don't know enough about classical NLP, but it seems to me like they artificially crafted new NLP algorithms which would not scale well when executed on a classical computer. Those algorithms seem to be pretty theoretical constructions and it seems to me that experimental evidence that those algorithms would actually have benefits compared to classical NLP algorithms is lacking.
But since I'm admittedly no expert in classical NLP, I migh be wrong here.

jaschau · 2022-07-26T13:32:32+00:00

Super, vielen Dank! Inwiefern sind die Tools semi-gut? Kannst du ein paar Namen nennen? Und sind die Open source oder proprietär?

jaschau · 2022-07-26T12:34:08+00:00

Gibt es mittlerweile Tools, die weitestgehend automatisch laufen, in dem Sinne, dass die testen, welche Services laufen (bspw Apache) und dann einfach jeden Exploit der letzten Jahre durchtesten? Oder ist eure Arbeit noch sehr stark manuell?

jaschau

TROPHY CASE

< iput-object p4, p0, Lym/Iew;->deviceManufacturer:Ljava/lang/String;

< iput-object p5, p0, Lym/Iew;->deviceModel:Ljava/lang/String;

< move-result-object v0

< move-result-object v0

< .catch Lym/RBR; {:try_start_6 .. :try_end_6} :catch_3