Datasets:

UniverseTBD
/

mmu_btsbot

_healpix_29 int64	band list	view list	array list	scale list	jd float32	diffmaglim float32	magpsf float32	sigmapsf float32	chipsf float32	magap float32	sigmagap float32	distnr float32	magnr float32	chinr float32	sharpnr float32	sky float32	magdiff float32	fwhm float32	classtar float32	mindtoedge float32	seeratio float32	magapbig float32	sigmagapbig float32	sgmag1 float32	srmag1 float32	simag1 float32	szmag1 float32	sgscore1 float32	distpsnr1 float32	jdstarthist float32	scorr float32	sgmag2 float32	srmag2 float32	simag2 float32	szmag2 float32	sgscore2 float32	distpsnr2 float32	sgmag3 float32	srmag3 float32	simag3 float32	szmag3 float32	sgscore3 float32	distpsnr3 float32	jdstartref float32	dsnrms float32	ssnrms float32	magzpsci float32	magzpsciunc float32	magzpscirms float32	clrcoeff float32	clrcounc float32	neargaia float32	neargaiabright float32	maggaia float32	maggaiabright float32	exptime float32	drb float32	acai_h float32	acai_v float32	acai_o float32	acai_n float32	acai_b float32	new_drb float32	peakmag float32	maxmag float32	peakmag_so_far float32	maxmag_so_far float32	age float32	days_since_peak float32	days_to_peak float32	ra float64	dec float64	label int64	fid int64	programid int64	field int64	nneg int64	ndethist int64	ncovhist int64	nmtchps int64	nnotdet int64	N int64	healpix int64	isdiffpos bool	is_SN bool	near_threshold bool	is_rise bool	OBJECT_ID_ string	source_set string	split string	object_id int64
5,205,237,378,833,472	[ "g", "g", "g" ]	[ "science", "reference", "difference" ]	[[[0.016353139653801918,0.012970472685992718,0.01546421367675066,0.015565923415124416,0.014651999808(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,787.75	19.974174	19.047394	0.175495	1.288261	18.947399	0.109	2.048233	19.702999	3.263	0.229	-0.186776	-0.099993	5.72	0.722	619.341492	0.64571	18.8521	0.1262	18.358601	17.6724	17.171801	16.962099	0.016667	3.982629	2,458,784	12.223401	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,315	4.238012	15.610688	26.131393	0.000116	0.056234	-0.080865	0.000135	3.97258	-999	18.348284	-999	30	0.998773	null	null	null	null	null	0.88416	18.255407	19.552307	18.618029	19.37566	3.986794	0.094838	3.891956	49.807125	7.586199	1	1	1	453	1	7	196	1	189	7	4	true	true	false	true	ZTF19acjdwqv	trues	train	1,033,361,844,915,015,000
5,205,237,378,833,472	[ "g", "g", "g" ]	[ "science", "reference", "difference" ]	[[[0.016353139653801918,0.012970472685992718,0.01546421367675066,0.015565923415124416,0.014651999808(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,787.75	19.974174	19.047394	0.175495	1.288261	18.947399	0.109	2.048233	19.702999	3.263	0.229	-0.186776	-0.099993	5.72	0.722	619.341492	0.64571	18.8521	0.1262	18.358601	17.6724	17.171801	16.962099	0.016667	3.982629	2,458,784	12.223401	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,315	4.238012	15.610688	26.131393	0.000116	0.056234	-0.080865	0.000135	3.97258	-999	18.348284	-999	30	0.998773	null	null	null	null	null	0.88416	18.255407	19.552307	18.618029	19.37566	3.986794	0.094838	3.891956	49.807125	7.586199	1	1	1	453	1	7	196	1	189	7	4	true	true	false	true	ZTF19acjdwqv	trues	train	1,033,361,844,915,015,000
5,205,237,401,799,872	[ "g", "g", "g" ]	[ "science", "reference", "difference" ]	[[[0.01549877692013979,0.017191827297210693,0.01415324117988348,0.013815924525260925,0.0173581205308(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,790.75	20.388239	18.721716	0.104373	5.443097	18.593201	0.0758	2.188034	19.702999	3.263	0.229	-0.022928	-0.128516	2.98	0.956	619.89563	0.899624	18.6231	0.0973	18.358601	17.6724	17.171801	16.962099	0.016667	3.987552	2,458,784	27.133568	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,315	22.195671	36.406235	26.146715	0.000099	0.063801	-0.125708	0.000106	3.977599	-999	18.348284	-999	30	0.999993	null	null	null	null	null	0.999228	18.255407	19.552307	18.558348	19.37566	6.940081	1.970949	4.969132	49.807023	7.586241	1	1	1	453	0	13	202	1	189	1	4	true	true	false	true	ZTF19acjdwqv	trues	train	1,036,315,124,915,015,000
5,205,237,401,799,872	[ "g", "g", "g" ]	[ "science", "reference", "difference" ]	[[[0.01549877692013979,0.017191827297210693,0.01415324117988348,0.013815924525260925,0.0173581205308(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,790.75	20.388239	18.721716	0.104373	5.443097	18.593201	0.0758	2.188034	19.702999	3.263	0.229	-0.022928	-0.128516	2.98	0.956	619.89563	0.899624	18.6231	0.0973	18.358601	17.6724	17.171801	16.962099	0.016667	3.987552	2,458,784	27.133568	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,315	22.195671	36.406235	26.146715	0.000099	0.063801	-0.125708	0.000106	3.977599	-999	18.348284	-999	30	0.999993	null	null	null	null	null	0.999228	18.255407	19.552307	18.558348	19.37566	6.940081	1.970949	4.969132	49.807023	7.586241	1	1	1	453	0	13	202	1	189	1	4	true	true	false	true	ZTF19acjdwqv	trues	train	1,036,315,124,915,015,000
5,205,237,403,041,193	[ "r", "r", "r" ]	[ "science", "reference", "difference" ]	[[[0.015435912646353245,0.014981232583522797,0.015921665355563164,0.015383007004857063,0.01459388807(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,811.75	19.972393	18.927689	0.126394	2.008826	18.781099	0.1265	3.858614	17.149	4.896	0.55	-0.117006	-0.146588	3.82	0.929	612.831482	0.728431	18.6768	0.1443	18.358601	17.6724	17.171801	16.962099	0.016667	3.933985	2,458,784	15.192198	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,320	9.864177	20.56189	26.045689	0.000035	0.038916	0.071129	0.000034	3.924109	-999	18.348284	-999	30	0.99997	null	null	null	null	null	0.993194	18.255407	19.552307	18.255407	19.552307	27.921875	13.964132	13.957743	49.806969	7.586242	1	2	1	453	0	27	216	1	189	2	4	true	true	false	false	ZTF19acjdwqv	trues	train	1,057,296,924,915,015,000
5,205,237,403,041,193	[ "r", "r", "r" ]	[ "science", "reference", "difference" ]	[[[0.015435912646353245,0.014981232583522797,0.015921665355563164,0.015383007004857063,0.01459388807(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,811.75	19.972393	18.927689	0.126394	2.008826	18.781099	0.1265	3.858614	17.149	4.896	0.55	-0.117006	-0.146588	3.82	0.929	612.831482	0.728431	18.6768	0.1443	18.358601	17.6724	17.171801	16.962099	0.016667	3.933985	2,458,784	15.192198	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,320	9.864177	20.56189	26.045689	0.000035	0.038916	0.071129	0.000034	3.924109	-999	18.348284	-999	30	0.99997	null	null	null	null	null	0.993194	18.255407	19.552307	18.255407	19.552307	27.921875	13.964132	13.957743	49.806969	7.586242	1	2	1	453	0	27	216	1	189	2	4	true	true	false	false	ZTF19acjdwqv	trues	train	1,057,296,924,915,015,000
5,205,237,403,063,475	[ "r", "r", "r" ]	[ "science", "reference", "difference" ]	[[[0.014207114465534687,0.013965998776257038,0.0154495220631361,0.015508471988141537,0.0144888777285(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,804.75	19.500937	18.561152	0.096714	0.91724	18.3794	0.1218	3.861133	17.149	4.896	0.55	-0.294751	-0.181752	3.1	0.982	628.335388	0.953702	18.267	0.1394	18.358601	17.6724	17.171801	16.962099	0.016667	3.934922	2,458,784	15.560242	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,320	10.843623	20.247196	26.043152	0.000029	0.039099	0.085744	0.000029	3.925054	-999	18.348284	-999	30	0.999807	null	null	null	null	null	0.999923	18.255407	19.552307	18.255407	19.37566	20.875069	6.917326	13.957743	49.806966	7.586243	1	2	1	453	1	22	211	1	189	5	4	true	true	false	false	ZTF19acjdwqv	trues	train	1,050,250,114,915,015,000
5,205,237,403,063,475	[ "r", "r", "r" ]	[ "science", "reference", "difference" ]	[[[0.014207114465534687,0.013965998776257038,0.0154495220631361,0.015508471988141537,0.0144888777285(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,804.75	19.500937	18.561152	0.096714	0.91724	18.3794	0.1218	3.861133	17.149	4.896	0.55	-0.294751	-0.181752	3.1	0.982	628.335388	0.953702	18.267	0.1394	18.358601	17.6724	17.171801	16.962099	0.016667	3.934922	2,458,784	15.560242	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,320	10.843623	20.247196	26.043152	0.000029	0.039099	0.085744	0.000029	3.925054	-999	18.348284	-999	30	0.999807	null	null	null	null	null	0.999923	18.255407	19.552307	18.255407	19.37566	20.875069	6.917326	13.957743	49.806966	7.586243	1	2	1	453	1	22	211	1	189	5	4	true	true	false	false	ZTF19acjdwqv	trues	train	1,050,250,114,915,015,000
5,205,237,403,065,760	[ "g", "g", "g" ]	[ "science", "reference", "difference" ]	[[[0.016165975481271744,0.014370942488312721,0.01851542666554451,0.010832424275577068,0.015321242623(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,805	19.433994	19.038977	0.15193	0.967592	18.839701	0.1771	2.236781	19.702999	3.263	0.229	-0.619414	-0.199277	3.63	0.977	615.539917	0.888208	18.9014	0.2395	18.358601	17.6724	17.171801	16.962099	0.016667	3.943643	2,458,784	10.575103	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,315	5.769595	12.325761	26.154978	0.000067	0.040599	-0.09129	0.000084	3.933778	-999	18.348284	-999	30	0.999937	null	null	null	null	null	0.999508	18.255407	19.552307	18.255407	19.37566	21.005312	7.047569	13.957743	49.806964	7.586246	1	1	1	453	3	23	212	1	189	6	4	true	true	false	false	ZTF19acjdwqv	trues	train	1,050,380,354,915,015,000
5,205,237,403,065,760	[ "g", "g", "g" ]	[ "science", "reference", "difference" ]	[[[0.016165975481271744,0.014370942488312721,0.01851542666554451,0.010832424275577068,0.015321242623(...TRUNCATED)	[ 1.0099999904632568, 1.0099999904632568, 1.0099999904632568 ]	2,458,805	19.433994	19.038977	0.15193	0.967592	18.839701	0.1771	2.236781	19.702999	3.263	0.229	-0.619414	-0.199277	3.63	0.977	615.539917	0.888208	18.9014	0.2395	18.358601	17.6724	17.171801	16.962099	0.016667	3.943643	2,458,784	10.575103	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	-999	2,458,315	5.769595	12.325761	26.154978	0.000067	0.040599	-0.09129	0.000084	3.933778	-999	18.348284	-999	30	0.999937	null	null	null	null	null	0.999508	18.255407	19.552307	18.255407	19.37566	21.005312	7.047569	13.957743	49.806964	7.586246	1	1	1	453	3	23	212	1	189	6	4	true	true	false	false	ZTF19acjdwqv	trues	train	1,050,380,354,915,015,000

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mmu_btsbot HATS Catalog Collection

This is the collection of HATS catalogs representing mmu_btsbot.

This dataset is part of the Multimodal Universe, a large-scale collection of multimodal astronomical data. For full details, see the paper: The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TBs of Astronomical Scientific Data.

Access the catalog

We recommend the use of the LSDB Python framework to access HATS catalogs. LSDB can be installed via pip install lsdb or conda install conda-forge::lsdb, see more details in the docs. The following code provides a minimal example of opening this catalog:

import lsdb

# Full sky coverage.
catalog = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_btsbot")
# One-degree cone.
catalog = lsdb.open_catalog(
    "https://huggingface.co/datasets/UniverseTBD/mmu_btsbot",
    search_filter=lsdb.ConeSearch(ra=1.0, dec=78.0, radius_arcsec=3600.0),
)

Each catalog in this collection is represented as a separate Apache Parquet dataset and can be accessed with a variety of tools, including pandas, pyarrow, dask, Spark, DuckDB.

File structure

This catalog is represented by the following files and directories:

collection.properties — textual metadata file describing the HATS collection of catalogs
mmu_btsbot — main HATS catalog directory
- dataset/ — Apache Parquet dataset directory for the main catalog
  - ... parquet metadata and data files in sub directories ...
- hats.properties — textual metadata file describing the main HATS catalog
- partition_info.csv — CSV file with a list of catalog HEALPix tiles (catalog partitions)
- skymap.fits — HEALPix skymap FITS file with row-counts per HEALPix tile of fixed order 10
mmu_btsbot_10arcs/ — default margin catalog to ensure data completeness in cross-matching, the margin threshold is 10.0 arcseconds
- ... margin catalog files and directories ...

Catalog metadata

Metadata of the main HATS catalog, excluding margins and indexes:

Number of rows	Number of columns	Number of partitions	Size on disk	HATS Builder
818,214	92	2,078	18.9 GiB	hats-import v0.7.1, hats v0.7.1

Catalog columns

The main HATS catalog contains the following columns:

Name	`_healpix_29`	`band`	`view`	`array`	`scale`	`jd`	`diffmaglim`	`magpsf`	`sigmapsf`	`chipsf`	`magap`	`sigmagap`	`distnr`	`magnr`	`chinr`	`sharpnr`	`sky`	`magdiff`	`fwhm`	`classtar`	`mindtoedge`	`seeratio`	`magapbig`	`sigmagapbig`	`sgmag1`	`srmag1`	`simag1`	`szmag1`	`sgscore1`	`distpsnr1`	`jdstarthist`	`scorr`	`sgmag2`	`srmag2`	`simag2`	`szmag2`	`sgscore2`	`distpsnr2`	`sgmag3`	`srmag3`	`simag3`	`szmag3`	`sgscore3`	`distpsnr3`	`jdstartref`	`dsnrms`	`ssnrms`	`magzpsci`	`magzpsciunc`	`magzpscirms`	`clrcoeff`	`clrcounc`	`neargaia`	`neargaiabright`	`maggaia`	`maggaiabright`	`exptime`	`drb`	`acai_h`	`acai_v`	`acai_o`	`acai_n`	`acai_b`	`new_drb`	`peakmag`	`maxmag`	`peakmag_so_far`	`maxmag_so_far`	`age`	`days_since_peak`	`days_to_peak`	`ra`	`dec`	`label`	`fid`	`programid`	`field`	`nneg`	`nbad`	`ndethist`	`ncovhist`	`nmtchps`	`nnotdet`	`N`	`healpix`	`isdiffpos`	`is_SN`	`near_threshold`	`is_rise`	`OBJECT_ID_`	`source_set`	`split`	`object_id`
Data Type	int64	list<element: string>	list<element: string>	list<element: list<element: list<element: float>>>	list<element: float>	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	float	double	double	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	int64	bool	bool	bool	bool	string	string	string	int64
Value count	818,214	2,454,642	2,454,642	N/A	2,454,642	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214	818,214
Example row	270210874492301921	[r, r, r]	[science, reference, difference]	[[[0.01562, 0.01536, 0.01607, 0.01597, … (63 total)], … (63 total)], …	[1.01, 1.01, 1.01]	2.46e+06	20.07	19.62	0.1505	1.172	19.69	0.2534	0.3195	19.37	3.737	0.527	0.09285	0.06265	2.08	0.979	468.5	1.341	19.48	0.2668	19.36	18.51	17.92	17.7	0.04042	2.737	2.459e+06	10.01	17.24	16.57	16.24	16.09	0.9923	12.25	20.73	20.13	19.93	19.83	0.766	13.17	2.458e+06	9.074	12.85	26.38	3.614e-06	0.02667	0.08524	6.388e-06	2.738	-999	19.5	-999	30	1	0.9969	0.0001698	8.935e-08	0.03556	8.846e-05	0.9999	18.36	20.36	18.36	20.36	53.82	41.92	11.9	0.5351	78.02	1	2	1	854	4	0	46	1494	8	1448	9	239	True	True	False	False	ZTF21acdjasw	trues	train	1776246164415015001
Minimum value	143192435166087	g	difference	N/A	1.0099999904632568	2458208.0	14.993193626403809	11.828742980957031	0.003804703475907445	0.061839427798986435	11.862600326538086	0.0010999999940395355	0.0005188093637116253	11.85200023651123	0.05999999865889549	-0.7940000295639038	-148.0614013671875	-0.6691054701805115	-0.9399999976158142	-0.0	10.00100040435791	-999.0	11.839099884033203	0.0010999999940395355	-999.0	-999.0	-999.0	-999.0	-0.0	0.00033156777499243617	2458042.0	5.000020980834961	-999.0	-999.0	-999.0	-999.0	-999.0	-999.0	-999.0	-999.0	-999.0	-999.0	-999.0	-999.0	2458154.5	-236.6349639892578	-2.804577350616455	21.37965202331543	7.099999947968172e-07	0.012311999686062336	-1.7358529567718506	1.4523000118060736e-06	-999.0	-999.0	-999.0	-999.0	30.0	-999.0	-0.0	-0.0	-0.0	-0.0	2.2476720598673186e-14	6.094935223188713e-09	10.285042762756348	12.511996269226074	10.285042762756348	12.511996269226074	-0.0	-0.0	-0.0	0.02237	-30.3120473	0	1	1	202	0	0	1	1	1	-62	1	0	True	False	False	False	ZTF17aaaamwo	dims	test	453441342515015017
Maximum value	3458697867235106154	r	science	N/A	1.0099999904632568	2460172.75	21.433073043823242	21.104284286499023	0.21714617311954498	9912.720703125	21.708900451660156	1.0729000568389893	26.693052291870117	23.976999282836914	13.935999870300293	1.4600000381469727	119.668701171875	0.9848169088363647	7.75	1.0	1535.1785888671875	11.868691444396973	21.631799697875977	1.669700026512146	27.165000915527344	27.964000701904297	26.252599716186523	22.355899810791016	1.0	29.914194107055664	2460166.75	536.3513793945312	27.81800079345703	24.626800537109375	23.552799224853516	22.370399475097656	1.0	29.998464584350586	27.92300033569336	24.05900001525879	24.337200164794922	23.570499420166016	1.0	29.999650955200195	2459138.75	7598.24658203125	14282.5029296875	27.262657165527344	0.6577643752098083	0.857014000415802	2.0040109157562256	2.592698097229004	89.99986267089844	89.99980926513672	21.386119842529297	13.999776840209961	30.0	1.0	1.0	0.9999991655349731	0.9999970197677612	0.9995384812355042	1.0	1.0	20.396743774414062	22.171342849731445	21.009519577026367	21.818437576293945	2128.044677734375	1942.9764404296875	2126.070068359375	359.9805578	87.9603521	1	2	1	1877	13	4	4314	10393	429	10308	2867	3071	True	True	True	True	ZTF23aawwabr	vars	val	2418290236115015016

"Value count" may be different from the total number of rows for nested columns: each nested element is counted as a single value.

Crossmatch with another catalog

HATS catalogs can be efficiently crossmatched using LSDB, which leverages the HEALPix partitioning to avoid loading the full datasets into memory:

import lsdb

mmu_btsbot = lsdb.open_catalog("https://huggingface.co/datasets/UniverseTBD/mmu_btsbot")
other = lsdb.open_catalog("https://huggingface.co/datasets/<org>/<other_catalog>")

crossmatched = mmu_btsbot.crossmatch(other, radius_arcsec=1.0)
print(crossmatched)

See the LSDB documentation for more details on crossmatching and other operations.

Dataset-specific context

Original survey
This dataset comes from the Zwicky Transient Facility (ZTF), specifically the Bright Transient Survey (BTS), which scans the Northern sky every 2–3 nights to detect and study bright, nearby transient events.

Data modality
The dataset is multimodal, combining image triplets (science, reference, and difference) with tabular metadata containing extracted features of each transient candidate.

Typical use cases
It is mainly used for detecting and classifying astrophysical transients, particularly distinguishing real events from bogus detections and supporting follow-up observations. The dataset was also used to develop the BTSbot model described in the original paper.

Caveats
Each sample corresponds to an alert rather than a unique object, so the same transient may appear multiple times. Early detections are typically noisier and harder to classify, and the dataset is biased toward bright, local transients matching BTS selection criteria.

Citation
Users should cite the ZTF/BTS survey and the BTSbot paper. The dataset is released under the CC BY 4.0 license, requiring attribution to the original authors.

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Collection including UniverseTBD/mmu_btsbot

Multimodal Universe 🫶 LSDB/HATS 🎩 💫

Collection

Multimodal Universe Datasets in HATS format (https://hats.readthedocs.io/) • 37 items • Updated 14 days ago • 3

Papers for UniverseTBD/mmu_btsbot

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data

Paper • 2412.02527 • Published Dec 3, 2024 • 13

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Paper • 2401.15167 • Published Jan 26, 2024