id stringlengths 66 88 | text stringlengths 46 1.7k | duration float64 2.32 30 | dnsmos float64 2.8 3.65 | sampling_rate int64 24k 24k | path stringlengths 79 101 | audio_content_token_indices listlengths 58 749 | audio_global_embedding listlengths 128 128 | audio_token_len int64 58 749 | audio_duration float64 2.32 30 |
|---|---|---|---|---|---|---|---|---|---|
bilibili_data_1622095_BV1dZ421J7JT_p38_BV1dZ421J7JT_p38_m4-dialogue_0622695 | [S1] They did. It was a great game. The Jays have a new pitcher and he's fantastic. What about you? Do you like to watch baseball? [S2] Pitcher. [S1] I think baseball games take too long. I don't have the patience for it. I like the fast pace of basketball. I like to watch NBA games. | 24.32 | 2.9664 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p38_BV1dZ421J7JT_p38_m4-dialogue_0622695.mp3 | [
9818,
561,
7936,
6119,
1500,
4548,
5061,
4618,
7872,
3550,
6559,
11163,
11041,
11106,
11622,
2015,
2013,
1005,
3651,
1300,
9443,
12570,
8418,
9825,
11412,
9313,
6757,
12182,
11162,
12499,
1153,
3220,
3299,
2670,
8439,
9015,
9070,
11630,
116... | [
0.5482810735702515,
-5.709695816040039,
0.03976389020681381,
0.3816370666027069,
0.568026602268219,
0.021455101668834686,
0.18662939965724945,
-0.04409167170524597,
0.1937228888273239,
-3.9149818420410156,
0.29500776529312134,
-0.0033186418004333973,
-0.053534653037786484,
0.07132484018802... | 608 | 24.32 |
bilibili_data_1622095_BV1dZ421J7JT_p38_BV1dZ421J7JT_p38_m4-dialogue_0622696 | [S1] Pace. [S2] Got it. Next spring, we can watch the NBA finals together on TV. [S1] Sure thing. I look forward to it. | 9.96 | 3.128996 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p38_BV1dZ421J7JT_p38_m4-dialogue_0622696.mp3 | [
6867,
9248,
12563,
9904,
8098,
7602,
10227,
9191,
11156,
8604,
11659,
9621,
9484,
6988,
6483,
3418,
3418,
3418,
2913,
3360,
3946,
7010,
6515,
9953,
12377,
9736,
12499,
12633,
9769,
12498,
11987,
9866,
11924,
10083,
9882,
4635,
12573,
12563,
... | [
0.669719398021698,
-5.814965724945068,
-0.015439272858202457,
0.44194069504737854,
0.3749578595161438,
0.011048446409404278,
0.27468159794807434,
-0.06476051360368729,
0.19006407260894775,
-3.9862165451049805,
0.15848605334758759,
0.0833197608590126,
-0.16347967088222504,
0.029708739370107... | 249 | 9.96 |
bilibili_data_1622095_BV1dZ421J7JT_p39_BV1dZ421J7JT_p39_m4-dialogue_0692567 | [S1] Hmm. There's a new action film with The Rock. There's a new animation film from Disney. There are a few horror movies, and there's a romantic comedy with Will Smith. Action. [S2] Animation. [S1] Horror. [S2] Horror. [S1] Romantic comedy. | 24.08 | 2.934948 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p39_BV1dZ421J7JT_p39_m4-dialogue_0692567.mp3 | [
9363,
8342,
8342,
6295,
4247,
3799,
10069,
6878,
4255,
6871,
4309,
8939,
6763,
12626,
12442,
6675,
5729,
10005,
9363,
9781,
9436,
7404,
7265,
3113,
12506,
3609,
1429,
12726,
10582,
2949,
3854,
6080,
358,
1334,
3091,
8716,
9175,
10579,
10388... | [
0.18859045207500458,
-5.766045570373535,
0.12509410083293915,
0.21059550344944,
0.5784578323364258,
0.031011687591671944,
0.5227056741714478,
0.03265034034848213,
0.09263702481985092,
-3.915741205215454,
0.17057693004608154,
-0.03913243114948273,
-0.2747335731983185,
0.15779054164886475,
... | 602 | 24.08 |
bilibili_data_1622095_BV1dZ421J7JT_p42_BV1dZ421J7JT_p42_m4-dialogue_0046692 | [S1] You look excited. [S2] I am. I just bought tickets for Maroon 5's concert in Tokyo in December. [S1] Concert. [S2] Concert. [S1] I heard about their concert tour. Aren't they coming to Taipei, too? Why didn't you buy their Taipei concert tickets? [S2] Concert tour. [S1] Concert tour. | 20.16 | 3.13571 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p42_BV1dZ421J7JT_p42_m4-dialogue_0046692.mp3 | [
7186,
4688,
9987,
10059,
9604,
10515,
10394,
7770,
5330,
10547,
11630,
7329,
12562,
8686,
4055,
9940,
12644,
3562,
5984,
5465,
6096,
467,
829,
4538,
5075,
4494,
3203,
3487,
11679,
11171,
11851,
5378,
9564,
12642,
9450,
11859,
9843,
8986,
72... | [
0.05708961561322212,
-5.867303848266602,
0.03001466765999794,
0.30250483751296997,
0.45875877141952515,
-0.04125678166747093,
0.3952864706516266,
-0.08710160106420517,
0.12531256675720215,
-3.862858772277832,
0.04017629846930504,
0.009598132222890854,
-0.07860400527715683,
0.18809832632541... | 504 | 20.16 |
bilibili_data_1622095_BV1dZ421J7JT_p42_BV1dZ421J7JT_p42_m4-dialogue_0046693 | [S1] The Taipei concert tickets were sold out two hours after the tickets were released online. So I decided to try the Tokyo concerts instead. [S2] Sold out. [S1] Sold out. [S2] I see. How much were the tickets? [S1] $200 for floor seat tickets. [S2] Floor seat. | 20.56 | 3.152382 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p42_BV1dZ421J7JT_p42_m4-dialogue_0046693.mp3 | [
9745,
6192,
10892,
12442,
3648,
6519,
11934,
8216,
2944,
7604,
9721,
11708,
11130,
12137,
11667,
6242,
5225,
8360,
9655,
12653,
5015,
4559,
4682,
11371,
9187,
7161,
4538,
11573,
11708,
12077,
11787,
2832,
4066,
10653,
2241,
7250,
8592,
7660,
... | [
0.41633573174476624,
-5.784543514251709,
0.13472647964954376,
0.6330011487007141,
0.4261128008365631,
-0.2232176959514618,
0.36406686902046204,
-0.01129704900085926,
0.33538272976875305,
-3.904693841934204,
0.34042543172836304,
-0.19113634526729584,
-0.09721614420413971,
0.0946843698620796... | 514 | 20.56 |
bilibili_data_1622095_BV1dZ421J7JT_p42_BV1dZ421J7JT_p42_m4-dialogue_0046694 | [S1] That's a decent price. I paid $300 for Justin Bieber's concert last year. Will you stay a few more days in Tokyo? [S2] I might. Maybe I'll take two days off work and spend more time in Tokyo. [S1] You should. Tokyo is a fun city. | 18.48 | 2.92069 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p42_BV1dZ421J7JT_p42_m4-dialogue_0046694.mp3 | [
10075,
6762,
3802,
11058,
10992,
11883,
5272,
856,
1490,
10750,
4375,
5122,
8745,
7936,
7629,
7365,
10117,
5082,
8160,
8137,
3054,
4015,
6149,
4688,
7259,
10666,
4537,
4338,
2958,
6071,
4981,
12773,
12097,
12053,
6649,
6041,
3026,
5569,
685... | [
0.23776791989803314,
-5.888090133666992,
0.07817849516868591,
0.36539360880851746,
0.42844435572624207,
-0.0454690083861351,
0.3508727550506592,
-0.04376152902841568,
0.21039530634880066,
-3.8664939403533936,
0.1419040560722351,
0.08304032683372498,
-0.03757263720035553,
0.1025083586573600... | 462 | 18.48 |
bilibili_data_1622095_BV1dZ421J7JT_p44_BV1dZ421J7JT_p44_m4-dialogue_0046696 | [S1] 1245 PM. However, there are no more standard seats available on that train. The next train after that leaves at 2:00 PM. [S2] How long is the train ride? [S1] Three hours and 45 minutes. [S2] Okay. Two seats for 2:00 PM. [S1] Including the 5% tax, your total comes to €189. [S2] Tax. Here is my credit card. | 26.56 | 3.214309 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p44_BV1dZ421J7JT_p44_m4-dialogue_0046696.mp3 | [
11665,
4465,
243,
11566,
11120,
10992,
8355,
3811,
9081,
8560,
307,
10420,
8587,
8835,
12172,
12036,
3945,
5424,
4023,
3511,
6447,
12783,
4308,
6312,
10930,
5034,
12102,
7493,
3974,
1894,
4973,
4980,
1638,
8347,
12570,
9323,
7266,
12058,
98... | [
0.02125840075314045,
-5.6887617111206055,
0.15180981159210205,
0.34655505418777466,
0.4352559447288513,
-0.09629721939563751,
0.4049154222011566,
-0.09532783180475235,
-0.054389066994190216,
-3.862409830093384,
0.15052363276481628,
0.006735582835972309,
-0.15448909997940063,
0.180635064840... | 664 | 26.56 |
bilibili_data_1622095_BV1dZ421J7JT_p45_BV1dZ421J7JT_p45_m4-dialogue_0970269 | [S1] Keychain. [S2] Magnet. [S1] I think they are poorly made, and they aren't even made in France. [S2] Good point. What about getting some French desserts? [S1] I would love to bring some macarons home, but they would dry out quickly. | 18 | 2.88665 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p45_BV1dZ421J7JT_p45_m4-dialogue_0970269.mp3 | [
8986,
4392,
10132,
12675,
9538,
11717,
11717,
12055,
11649,
11736,
7634,
11741,
8686,
9190,
7638,
7621,
6606,
4550,
7246,
6213,
6222,
4267,
3763,
12506,
12633,
9825,
11473,
11986,
12417,
11924,
7523,
9883,
4645,
6732,
9908,
6356,
12555,
7261,... | [
0.35942161083221436,
-5.82248592376709,
-0.017389735206961632,
0.24313777685165405,
0.4985719621181488,
-0.10233939439058304,
0.3301963806152344,
-0.011403962969779968,
0.12970878183841705,
-3.9058926105499268,
0.21137635409832,
0.04903857782483101,
-0.27378496527671814,
0.1249155551195144... | 450 | 18 |
bilibili_data_1622095_BV1dZ421J7JT_p5_BV1dZ421J7JT_p5_m4-dialogue_0201314 | [S1] Wow, you're completely soaked. [S2] Soak. [S1] It's terrible out there. I had an umbrella, but it was useless against the heavy rain. | 12.84 | 2.85592 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p5_BV1dZ421J7JT_p5_m4-dialogue_0201314.mp3 | [
3219,
12571,
11794,
6193,
11979,
9363,
8357,
5732,
3875,
5859,
5355,
8041,
11048,
11625,
11754,
11700,
8114,
10610,
11059,
11571,
11006,
7871,
8319,
5230,
781,
6377,
11987,
11998,
8821,
4765,
12635,
9824,
6192,
12427,
3748,
10051,
10458,
7842... | [
0.5184734463691711,
-5.863399505615234,
0.006453359499573708,
0.5012847185134888,
0.38151106238365173,
-0.15754257142543793,
0.22642222046852112,
-0.06016193702816963,
0.2120511382818222,
-3.9025189876556396,
0.31124943494796753,
-0.08405259251594543,
-0.01980053447186947,
0.11719952523708... | 321 | 12.84 |
bilibili_data_1622095_BV1dZ421J7JT_p5_BV1dZ421J7JT_p5_m4-dialogue_0201315 | [S1] I can see that. Do you have a change of clothes? [S2] Change of- [S1] ... clothes. [S2] No, I don't. [S1] I have some extra clothes in my office. I started keeping shirts and pants in the office after I got soaked in a rainstorm last summer. [S2] Rainstorm. | 22.96 | 3.24977 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p5_BV1dZ421J7JT_p5_m4-dialogue_0201315.mp3 | [
9635,
3761,
8979,
9258,
8753,
11519,
5502,
9726,
12767,
2315,
11418,
5026,
7599,
4758,
5645,
3334,
2976,
6041,
5528,
6040,
3017,
2944,
3924,
11603,
11531,
11973,
7494,
3203,
3481,
4526,
4972,
4916,
4916,
7540,
2420,
7248,
3120,
7745,
6939,
... | [
0.324383407831192,
-5.698430061340332,
0.12160360813140869,
0.4939461052417755,
0.36038386821746826,
-0.2134370505809784,
0.1427895575761795,
-0.0013874166179448366,
0.15958808362483978,
-3.902622938156128,
0.34063902497291565,
-0.1393594592809677,
0.10836968570947647,
0.13442519307136536,... | 574 | 22.96 |
bilibili_data_1622095_BV1dZ421J7JT_p6_BV1dZ421J7JT_p6_m4-dialogue_0046697 | [S1] That's a good idea. [S2] But we'll have to wait about 25 minutes to get our food. [S1] That's okay. I can wait. [S2] What do you feel like eating? [S1] How about Thai food? [S2] Sure. There's a Thai restaurant on my food delivery app. It'll take about 30 minutes to get here. | 24.48 | 3.246412 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p6_BV1dZ421J7JT_p6_m4-dialogue_0046697.mp3 | [
9562,
3345,
4527,
7095,
12218,
12155,
8785,
3041,
2960,
8563,
9508,
3084,
8218,
8737,
11994,
6250,
11603,
3558,
3431,
11110,
12564,
2956,
11574,
8564,
12147,
10052,
1541,
8384,
6611,
10125,
11521,
11465,
11483,
6447,
878,
1908,
1318,
4315,
... | [
0.4091816842556,
-5.730954170227051,
-0.05262620002031326,
0.23275138437747955,
0.5333104729652405,
0.03487967699766159,
0.4818568527698517,
-0.03687272220849991,
0.15244616568088531,
-4.021839618682861,
0.24394115805625916,
0.12969526648521423,
-0.2536892294883728,
0.05649888515472412,
... | 612 | 24.48 |
bilibili_data_1622095_BV1dZ421J7JT_p7_BV1dZ421J7JT_p7_m4-dialogue_0656691 | [S1] Hi. How are you? [S2] I'm good, thank you. How's your day going? [S1] It's okay. Two more hours until I get off my shift. How would you like to pay today? Cash or card? Shift. [S2] Credit card. Thanks. [S1] Would you like any bags? [S2] Bag. [S1] No, thank you. I brought my own cloth bag. | 28.32 | 2.891843 | 24,000 | audio/en/bilibili_data_1622095_BV1dZ421J7JT_p7_BV1dZ421J7JT_p7_m4-dialogue_0656691.mp3 | [
7201,
2593,
7412,
1470,
3965,
3965,
3958,
4470,
4534,
4023,
7151,
7654,
9678,
9095,
4950,
7604,
5043,
9570,
9891,
7275,
11987,
12642,
7202,
4145,
9996,
9730,
4617,
9771,
12634,
4186,
2210,
4323,
7671,
4541,
8445,
5222,
5879,
11510,
9525,
... | [
0.2410508394241333,
-5.774068355560303,
0.19051212072372437,
0.03280433639883995,
0.5630646347999573,
-0.05821561813354492,
0.2995927929878235,
-0.055725548416376114,
0.07685129344463348,
-3.8456215858459473,
0.21566306054592133,
-0.07999109476804733,
-0.1547020971775055,
0.224105626344680... | 708 | 28.32 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512258 | [S1] Yeah, uh, first of all, I think that you did a great job in, uh, describing the overall paper and I have almost, uh, no, you know, I have almost nothing to- [S2] No complaints. [S1] Yeah, no complaints regarding that. And, uh, maybe one point regarding the overall, uh, overall point of the paper. And yeah, it, as it's seen from the title, uh, Fourier Convolution | 29.52 | 3.29129 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512258.mp3 | [
6744,
1365,
11588,
11684,
12141,
4023,
4838,
12542,
4030,
6525,
6590,
6006,
5502,
5494,
6372,
5864,
6314,
6764,
5739,
4201,
5561,
485,
8486,
3352,
3137,
2976,
10402,
2730,
2673,
3247,
3247,
5814,
10426,
10264,
5257,
6374,
8958,
6910,
3950,
... | [
1.0480505228042603,
-5.554858684539795,
-0.13236895203590393,
-0.05074971914291382,
0.5221347808837891,
-0.1340174525976181,
0.4218723475933075,
0.016500283032655716,
-0.08695684373378754,
-3.7462968826293945,
-0.09876541793346405,
-0.039127059280872345,
-0.09129263460636139,
0.15987136960... | 738 | 29.52 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512259 | [S1] So these would be the, the Fourier convolutions. Was it already in the form that we see in the paper with the two strands of information- [S2] Yeah. [S1] ... like the global and the local, or did you have to shake things up? [S2] Uh, no, this, uh, this, uh, the right part of this, uh, feature is, uh, reflect the original, uh, form of this fast Fourier convolution as it was proposed by the authors. [S1] Cool. And, um, did it work out of the box? | 28.08 | 3.261655 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512259.mp3 | [
6314,
6713,
1241,
3408,
476,
8286,
2688,
11682,
11585,
6475,
2944,
5348,
10540,
6209,
6184,
9130,
12171,
12173,
4612,
2833,
12198,
11654,
12102,
9094,
3975,
4486,
9094,
1932,
1627,
9745,
8353,
9290,
5754,
2816,
9566,
12037,
10054,
4486,
506... | [
0.8133490085601807,
-5.471554279327393,
0.0044728834182024,
-0.12882287800312042,
0.36376431584358215,
-0.048430636525154114,
0.3553517162799835,
-0.0011993078514933586,
-0.17980408668518066,
-3.82486629486084,
0.11994793266057968,
0.19646614789962769,
-0.005011136643588543,
0.184182405471... | 702 | 28.08 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512262 | [S1] Is your, is your algorithm deterministic? [S2] Yeah. [S1] If, if I give it the same input and the same mask, so if I, and this is, is this correct, the, the cleanup.pictures app, that is really your small model that runs here? [S2] No, the, this is the large, large model. [S1] Oh, this is the big model already. [S2] Yeah. [S1] Okay. So here, you know, I've, I've taken this, but what happens, have you ever tried just masking the whole picture? What's kind of like the default output? | 29.52 | 3.140312 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512262.mp3 | [
2601,
11299,
12693,
12107,
6404,
449,
2945,
1346,
12548,
10645,
10340,
7836,
10985,
8017,
8017,
5515,
853,
4708,
11874,
11891,
9370,
8810,
9888,
11493,
12749,
1418,
2888,
9538,
10473,
2899,
4315,
9843,
11314,
9150,
6590,
8511,
7973,
9557,
5... | [
0.45274603366851807,
-5.607014179229736,
0.07395307719707489,
-0.1573854237794876,
0.47170281410217285,
0.07110920548439026,
0.2937261760234833,
0.024848591536283493,
-0.11464497447013855,
-3.8267722129821777,
0.20130087435245514,
0.13974446058273315,
0.10083893686532974,
0.216416701674461... | 738 | 29.52 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512263 | [S1] commonly used, uh, this is a commonly used, uh, loss in image-to-image tasks. [S2] Mm-hmm. [S1] It helps to stabilize, stabilize training. [S2] So you, the idea is that the discriminator bases its decisions on features which are perceptually meaningful. So very, very similar to the perceptive loss that you have up here. | 23.88 | 3.12903 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512263.mp3 | [
6184,
3681,
12562,
4588,
10492,
7912,
10778,
10412,
11060,
11573,
8717,
8846,
8845,
3285,
5900,
11669,
11588,
12103,
6916,
7314,
12163,
10577,
10394,
9047,
5074,
1240,
4305,
8224,
5145,
5952,
721,
4693,
7390,
3934,
3926,
3934,
3934,
3934,
3... | [
0.6646831631660461,
-5.6295247077941895,
-0.04336542263627052,
-0.115854412317276,
0.43483132123947144,
-0.08391512185335159,
0.3057698607444763,
-0.01525088306516409,
-0.10444076359272003,
-3.792548418045044,
0.13076439499855042,
0.022470546886324883,
0.10815896093845367,
0.08512721955776... | 597 | 23.88 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512264 | [S1] Which seven losses go into my final loss, right? Of the 50 possible losses that I could do, do I try them all or, or they're some, some guidelines? [S2] Actually, I think, uh, all of these losses, except for high receptive field, perceptual loss are pretty common. [S1] Mm-hmm. [S2] And they all, | 22.44 | 2.98572 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512264.mp3 | [
3689,
5688,
2070,
557,
5357,
11731,
10068,
10264,
12162,
12682,
12754,
10193,
4457,
11299,
9779,
5202,
5035,
8169,
8160,
8080,
8677,
11766,
12269,
2779,
2925,
10973,
11283,
11342,
8847,
11407,
7252,
9506,
9825,
6219,
2709,
2707,
5468,
7806,
... | [
0.6418371200561523,
-5.770888328552246,
-0.10892504453659058,
0.017586059868335724,
0.5740107297897339,
0.0906253457069397,
0.40141600370407104,
0.024585023522377014,
0.11428800225257874,
-3.6953394412994385,
0.07873241603374481,
-0.08241575956344604,
0.03422131761908531,
0.176217466592788... | 561 | 22.44 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512266 | [S1] ... reconstruct something that we can reconstruct, so we need some loss for reconstruction. And ad-ad-in loss is too restrictive, so we need something that works on features. [S2] Yeah. [S1] Uh, but we worked a lot on... We made a hyper-parameter search, of course, and we changed our | 23.56 | 2.837151 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512266.mp3 | [
5672,
9738,
6758,
4380,
6556,
6246,
11994,
5558,
11509,
8789,
4010,
5592,
6785,
3011,
11629,
11186,
10739,
9006,
11427,
6554,
2984,
2896,
3034,
11198,
8574,
6966,
9309,
6433,
3808,
5862,
8213,
3171,
3171,
802,
6959,
6959,
7242,
8272,
3913,
... | [
0.8717641830444336,
-5.678571701049805,
-0.01919393427670002,
0.09066331386566162,
0.4133071005344391,
-0.15630970895290375,
0.7064168453216553,
0.017409419640898705,
0.15350086987018585,
-3.873483896255493,
0.1774662733078003,
-0.36586853861808777,
-0.45246386528015137,
0.1857359111309051... | 589 | 23.56 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512267 | [S1] Yeah, yeah, because it is natural for the segmentation model to focus more on boundaries of objects instead of their textures. [S2] Yeah. [S1] And, uh, in case of in-painting, good texture can be, uh, learned using only discriminator. [S2] Mm-hmm. [S1] Because there is a lot of freedom in how we can generate fine-grained textures and there is no need to put some, any supervision on that part of, uh, the image. [S2] But the- | 28.48 | 3.305982 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512267.mp3 | [
6817,
1028,
4821,
12181,
11628,
9071,
7022,
6244,
9386,
12338,
8361,
561,
7240,
1612,
12173,
11693,
7095,
6892,
3683,
6754,
6126,
7365,
10858,
7584,
11062,
10483,
10547,
10613,
5964,
2905,
3464,
385,
5918,
11109,
9485,
12611,
12165,
7373,
1... | [
0.8822304010391235,
-5.502527236938477,
0.08922626823186874,
-0.1707010567188263,
0.5550698637962341,
-0.25809672474861145,
0.3391384780406952,
0.07206784188747406,
-0.15710915625095367,
-3.795036792755127,
0.19228722155094147,
-0.16548795998096466,
-0.13972900807857513,
-0.030416954308748... | 712 | 28.48 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512269 | [S1] ... cool. [S2] Unfortunately, I'm not familiar with, uh, word-shaping from reinforcement learning. [S1] Yeah. [S2] Uh, but our idea here was that basically we have two losses here. The first one is discriminator or the serial and which focused more on fine-grained details and the second is perceptual loss which focused more on, uh, global text, global structures. Yeah. [S1] Mm. | 25.04 | 3.286873 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512269.mp3 | [
6364,
9676,
7333,
11197,
9148,
6443,
5279,
2710,
30,
3350,
6811,
9379,
9811,
8291,
9883,
6170,
8962,
7461,
9907,
6250,
8853,
10077,
9965,
10166,
3965,
6526,
11293,
9360,
5426,
3451,
8037,
9537,
6984,
6361,
5773,
3923,
12215,
8125,
11772,
... | [
0.9573473334312439,
-5.4201555252075195,
-0.14909744262695312,
-0.02731487713754177,
0.5601029992103577,
-0.24507641792297363,
0.36621448397636414,
0.037725165486335754,
-0.18749497830867767,
-3.7858099937438965,
0.20392285287380219,
-0.10044527798891068,
-0.02744149975478649,
-0.016928760... | 626 | 25.04 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512270 | [S1] This up here is really beautiful, right? [S2] Yeah. [S1] What, what picture could I take such that it is absolute garbage? [S2] Yeah, actually, lots of, lots of images, uh, will be, uh, processed bad with our model. Yeah. [S1] I mean, the part of, of course, I can give it a picture that is, you know, very dissimilar to the training data set. [S2] Yeah. [S1] But let's say I actually had a training data set. What, what would be the worst domain or the worst kind of picture? [S2] Yeah, um, | 29.92 | 3.147766 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512270.mp3 | [
9769,
1065,
2963,
11241,
6499,
5528,
786,
3959,
317,
4138,
8497,
7522,
12676,
11163,
2998,
258,
1445,
4374,
448,
5058,
733,
2850,
354,
339,
11590,
11653,
6237,
2771,
4879,
7493,
1057,
6497,
12549,
3863,
2830,
4683,
982,
1310,
3944,
6000,
... | [
0.5869483351707458,
-5.55074405670166,
0.03991246595978737,
-0.20395343005657196,
0.5455595254898071,
0.05763059854507446,
0.2600492238998413,
0.041432637721300125,
-0.07712685316801071,
-3.862276554107666,
0.10211002081632614,
0.11085718125104904,
0.05261065065860748,
0.16811923682689667,... | 748 | 29.92 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512271 | [S1] I think- [S2] Yeah. Please, go. [S1] ... it cannot, uh, recreate half of human on something. [S2] Mm-hmm. Yeah, our model focuses mostly on background due to how it was trained and- [S1] Yeah. [S2] ... yeah, it cannot, uh, recover foreground objects really well. [S1] It cannot, uh, do something that requires it to actually know everything about walls and not just take it from picture it sees. | 29 | 2.911455 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512271.mp3 | [
9064,
4383,
3101,
9014,
10681,
12267,
6491,
867,
3310,
6489,
11241,
10648,
12049,
6283,
6802,
6298,
5778,
3931,
6363,
6827,
8803,
9251,
9235,
8819,
6859,
6419,
3365,
6373,
5659,
6689,
7410,
9376,
2601,
4634,
9440,
6187,
4251,
3603,
4130,
... | [
0.8478728532791138,
-5.590081691741943,
-0.10900361835956573,
-0.004130263347178698,
0.7950568795204163,
-0.009513458237051964,
0.6591506600379944,
0.052171241492033005,
0.13233549892902374,
-3.8065826892852783,
-0.07626237720251083,
-0.32012465596199036,
-0.2637772560119629,
0.11661227792... | 725 | 29 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512272 | [S1] Yeah, I have, I have, um, another question and to the Fourier convolution. So, here we have global information and, and local information, right? As, as sort of two different things. You mention in the paper that other models that have more global information or access to wider information could also work, such as a vision transformer or something like this. [S2] Yeah. [S1] My question is, is there an in-between between | 28.92 | 3.233119 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512272.mp3 | [
8745,
6754,
8725,
9924,
11147,
10739,
6134,
7211,
8702,
11702,
7523,
11707,
8996,
3056,
3747,
8753,
8502,
10021,
9643,
11179,
5979,
1002,
11566,
8175,
4015,
6575,
3503,
4014,
4023,
4462,
5661,
6255,
6751,
9836,
12450,
12395,
562,
8229,
4335... | [
0.5889399647712708,
-5.598641872406006,
0.07529449462890625,
-0.2216443121433258,
0.42385974526405334,
0.1623639464378357,
0.2719716727733612,
0.03058655932545662,
-0.10139721632003784,
-3.7776384353637695,
0.20698139071464539,
0.16661085188388824,
0.14043591916561127,
0.16133876144886017,... | 723 | 28.92 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512273 | [S1] Yeah, it doesn't scale up, uh, perfectly, but yeah, it, it, it better than, uh, fully convolution architectures. [S2] Cool. Um, yeah, so where do you think, I mean, m- maybe you don't wanna disclose necessarily, but, but what, what do you, what is the plan for the future? [S1] [LAUGHS] | 16.08 | 3.164055 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512273.mp3 | [
3713,
6990,
9654,
4007,
4998,
9941,
6208,
8157,
10617,
11067,
3857,
401,
8342,
10839,
11787,
8328,
1361,
3809,
3280,
4312,
12443,
5099,
12197,
11675,
11668,
11677,
7983,
10487,
7798,
7774,
2758,
3933,
9900,
11893,
6078,
8062,
5879,
8298,
62... | [
0.8323982954025269,
-5.626744747161865,
-0.13656839728355408,
-0.025897255167365074,
0.3949830234050751,
0.039904262870550156,
0.30675962567329407,
-0.04633191600441933,
-0.07699225842952728,
-3.8217389583587646,
0.06123282015323639,
0.1653996706008911,
0.05427901819348335,
0.0742170661687... | 402 | 16.08 |
bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512275 | [S1] Yeah. I mean, I, I was almost, I was expecting you to say, we're not happy with our loss. We want more. We want, like, more components to make it be- but it's, it's, I, I think it's pretty cool that, uh, the goal is also to make a system that's kind of as good but simpler. [S2] Yeah. [S1] Um, I think that'll make it also much more accessible. [S2] Cool. [S1] Yeah, um, uh, Roman, Elisa, sorry, Lisa, is that correct? [S2] Yeah. | 25.76 | 3.169234 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p160_BV17h4y1j7aZ_p160_m4-dialogue_0512275.mp3 | [
3229,
8737,
8810,
8345,
9322,
8730,
7382,
12165,
10717,
12207,
1527,
1460,
1846,
4908,
7401,
12386,
12338,
8865,
9825,
6249,
8796,
6889,
5234,
12398,
12774,
3877,
553,
3683,
12196,
8564,
5156,
12693,
12098,
11668,
8206,
8387,
7329,
8753,
11... | [
0.5494576096534729,
-5.7130255699157715,
0.07969602942466736,
-0.16266003251075745,
0.3571113049983978,
0.03818568214774132,
0.32909300923347473,
0.04082368314266205,
-0.07896967232227325,
-3.765490770339966,
0.2673025131225586,
0.25248268246650696,
0.05467677488923073,
0.2800549864768982,... | 644 | 25.76 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758465 | [S1] ... through the paper. So, Martin, uh, welcome. Uh, thank you very much for being here. [S2] Hey, I'm happy to be here. [S1] Um, did, was, was it a sort of a good description of what I said so far about Player of Games? [S2] Oh, yes, very, very, very much so. | 16.92 | 3.153081 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758465.mp3 | [
7259,
4325,
7136,
6560,
3360,
885,
3866,
2910,
7758,
5189,
5189,
2694,
1685,
9900,
6705,
2050,
403,
4838,
8296,
7665,
10165,
5085,
10182,
7308,
8296,
3563,
5999,
396,
1411,
4820,
4467,
8873,
5716,
7262,
12404,
7269,
7469,
9326,
5229,
9838... | [
0.7047213912010193,
-5.880446910858154,
-0.12534913420677185,
0.06770626455545425,
0.6807475686073303,
0.015060819685459137,
0.27985936403274536,
-0.11536175012588501,
-0.14039714634418488,
-3.7663636207580566,
0.16842107474803925,
0.03230222687125206,
-0.0295545756816864,
0.05048104003071... | 423 | 16.92 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758466 | [S1] ... that, uh, we will see in this paper, which is it introduced this notion of, uh, of local search in, uh, in poker and these value functions. [S2] Mm-hmm. [S1] And Play of Games is really just putting together AlphaZero in deep step into a single big unified, uh, algorithm. So the, the, the, let's maybe start with the, with the component that, that you, that you just talked about, which is, uh, value function. | 25.28 | 2.834349 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758466.mp3 | [
12683,
9167,
6071,
9254,
8397,
448,
18,
2754,
9591,
12783,
7454,
10841,
6241,
11712,
6502,
8551,
8423,
5790,
3741,
3307,
12614,
7429,
3307,
8511,
8413,
7593,
8080,
3969,
12102,
7429,
9956,
6166,
5514,
10142,
6048,
6208,
8808,
4021,
7653,
... | [
0.1734119951725006,
-5.927214622497559,
0.06734319031238556,
-0.43516165018081665,
0.3878454566001892,
-0.15108156204223633,
0.5251206159591675,
0.027119005098938942,
-0.2998664081096649,
-4.0269646644592285,
0.20864038169384003,
-0.27901792526245117,
-0.21467077732086182,
0.28439769148826... | 632 | 25.28 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758468 | [S1] It's, it makes, uh, little to no sense to say what's a value, what's a value of a sub-game or sub-problem in a poker where, where I hold a pair of, of aces. That's pretty much ill-defined, uh, ill-defined sub-game. [S2] Mm-hmm. [S1] But what we, what you need to do is given a, given a public, uh, state, which is, as you say, I come to a table, I see everything that, that I could have observed as a public observer. So that, that's, that, uh, that's basically my state. But given this state, given this observation, | 29.92 | 2.913961 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758468.mp3 | [
12516,
12742,
9737,
8857,
8449,
2904,
3288,
11882,
3218,
6748,
6762,
9812,
8925,
9414,
7688,
8216,
6761,
522,
5214,
8229,
11254,
10219,
12756,
9377,
11875,
4586,
5984,
2881,
2972,
11045,
11046,
10973,
10534,
5926,
5934,
1244,
3741,
6301,
37... | [
0.1489231437444687,
-5.878063201904297,
0.1515377014875412,
-0.5165203809738159,
0.5444222688674927,
-0.10974038392305374,
0.49046510457992554,
-0.018987225368618965,
-0.36709660291671753,
-4.024369716644287,
0.2543775141239166,
-0.30070891976356506,
-0.1778961420059204,
0.3222185373306274... | 748 | 29.92 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758470 | [S1] Yeah, I see. Okay. And you have this network and you train it, uh, in some way via, via self play. And now we get to the part where you generalize this search procedure, right? And- [S2] Mm-hmm. [S1] ... let me see. Oh, this is here. [S2] Yes. [S1] So this search procedure, as we said, in, in alpha, uh, again, in alpha zero, you have something like, you | 22.2 | 3.346411 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758470.mp3 | [
8232,
6201,
4227,
9164,
10732,
4022,
1641,
4708,
8802,
12394,
11827,
11289,
9975,
3965,
10126,
4008,
2976,
346,
2382,
4994,
4292,
3107,
12395,
10993,
9265,
8330,
12691,
7476,
12526,
7278,
9838,
3180,
4653,
1573,
3794,
9362,
11966,
7982,
369... | [
0.8649237155914307,
-5.67995548248291,
-0.06078379973769188,
0.13706083595752716,
0.4224010407924652,
0.00014074333012104034,
0.11433828622102737,
-0.0965011864900589,
-0.10217013210058212,
-3.73565411567688,
0.23447869718074799,
0.0815662369132042,
0.07701841741800308,
0.04024664685130119... | 555 | 22.2 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758471 | [S1] You would, you would sort of, you would make many iterations, let's say 50 iterations or something like this. In every iteration, you'd go down the tree and you find a node that you haven't expanded yet and you'd expand that node, right? [S2] [LAUGHS] [S1] In, in, in player of games, this is quite a bit more intricate, right? Uh, as, as we also have | 21.56 | 3.133394 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758471.mp3 | [
6697,
7699,
9300,
9995,
11715,
10653,
2646,
2804,
10540,
10667,
9227,
8129,
4246,
8753,
6805,
11715,
8405,
5294,
10668,
7243,
5522,
8152,
5548,
8059,
3845,
8157,
7981,
8689,
4393,
8225,
11865,
11378,
12441,
8761,
8731,
12548,
11212,
10453,
... | [
0.6761842966079712,
-5.74947452545166,
-0.0790044292807579,
0.1153910681605339,
0.46363377571105957,
0.023331545293331146,
0.1790057122707367,
-0.1049560010433197,
-0.19399867951869965,
-3.7593936920166016,
0.2696816921234131,
0.10885990411043167,
-0.02529122494161129,
0.03421109914779663,... | 539 | 21.56 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758472 | [S1] and improve the policy of both players, right? And it just does this for many, many iterations. It improves here, here, here, everywhere in the game tree until the whole game tree is, uh, approximately optimal. [S2] [LAUGHS] [S1] And the, the biggest game that has been solved so far, if you describe this in the paper, is Limit, Limit Heads Up Hold 'Em, is that correct? [S2] Yes. [S1] Fix Limit Hold 'Em. Yeah. [S2] Exactly. [S1] That's, that's actually a solved game. | 26.4 | 3.335737 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758472.mp3 | [
6176,
6761,
10862,
11766,
9199,
7151,
7159,
7151,
6966,
8213,
8271,
8847,
9286,
5633,
8642,
4379,
10794,
7849,
9777,
7721,
11550,
12749,
9734,
8237,
5672,
10538,
9714,
1010,
2900,
2900,
10396,
7822,
6,
646,
795,
5616,
8688,
8177,
5624,
35... | [
0.7156736850738525,
-5.587573528289795,
0.002982513280585408,
0.007445030380040407,
0.5356330275535583,
0.024717509746551514,
0.13506071269512177,
-0.08163948357105255,
-0.12787571549415588,
-3.76267147064209,
0.19723601639270782,
0.08683522790670395,
0.12096110731363297,
-0.00801405124366... | 660 | 26.4 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758475 | [S1] Get the entire tree in order again. So you expand the new node and then you have to do the whole update of the whole tree for a bunch of iterations before you can expand another one, such that everything, like, stays consistent. [S2] Mm-hmm. [S1] Yeah. Okay. That's, I mean, this, this, it gives a bit of an impression of why this is, uh, much more, much more complex, right? | 21.88 | 3.239764 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758475.mp3 | [
5737,
11346,
12251,
11251,
12781,
11792,
8337,
8793,
3609,
5322,
10685,
6444,
6516,
7340,
11863,
9240,
5248,
6633,
9597,
5565,
12130,
12099,
11685,
5469,
772,
5737,
11202,
8680,
8045,
2891,
11604,
12484,
9924,
7493,
9199,
7382,
8909,
3767,
... | [
0.7681631445884705,
-5.703742980957031,
-0.10231509804725647,
-0.03337116912007332,
0.5075267553329468,
0.10759980976581573,
0.26688358187675476,
-0.06113842502236366,
-0.13043507933616638,
-3.724999189376831,
0.12453549355268478,
0.12299969047307968,
0.13823114335536957,
0.023008679971098... | 547 | 21.88 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758476 | [S1] Mm-hmm. So you collect the train. It's similar to Alpha Zero, as you say. You collect the training set as you go. So the training set for the next iteration is whatever the network had to do during this iteration. So it's not just a random sample of states. [S2] Yeah. [S1] And you train in the same manner as Alpha Zero. You train to predict your own future outputs. Is that approximate? So if, uh, let's, let's distinguish. If | 28.2 | 3.279196 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758476.mp3 | [
8274,
11031,
8852,
8918,
4821,
6803,
7332,
9810,
12338,
12322,
7769,
6690,
9426,
10131,
4717,
4721,
4242,
3552,
3044,
10550,
4300,
12164,
10510,
11427,
10216,
5493,
10990,
2709,
5982,
11764,
11243,
7460,
10851,
9699,
4769,
8320,
2961,
3281,
... | [
0.6245024800300598,
-5.537537097930908,
0.028564058244228363,
-0.09301828593015671,
0.5448161959648132,
-0.008191517554223537,
0.2078540027141571,
-0.03606230765581131,
-0.1547589749097824,
-3.8021130561828613,
0.25089919567108154,
0.1390068233013153,
0.14239872992038727,
0.075375877320766... | 705 | 28.2 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758477 | [S1] ... like one or two or three steps in the future, you actually win or lose the game, you can train on your reward of the game. But Alpha Zero also, if it doesn't win or lose the game in the next step or so, it tries to predict its own output. So it, it tries to improve that way using TD Lambda. Um, and you here have a, a TD1, right? Um, so your, your targets, what do you target? What do you- [S2] Yeah. [S1] ... give the network as labels? | 28.2 | 3.442012 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758477.mp3 | [
9377,
12059,
11995,
5981,
8183,
5094,
7314,
12587,
3747,
2733,
2739,
7982,
11188,
8436,
11060,
12309,
8022,
10550,
5534,
843,
5225,
11729,
8684,
10443,
2693,
8999,
5495,
3028,
4962,
5984,
946,
796,
2965,
11979,
12677,
6625,
10729,
6544,
277... | [
0.7936699986457825,
-5.51356840133667,
0.056436315178871155,
-0.030230702832341194,
0.5395909547805786,
0.012238852679729462,
0.10403571277856827,
-0.025241291150450706,
-0.1563931703567505,
-3.8045663833618164,
0.25386330485343933,
0.17724861204624176,
0.07260702550411224,
0.0031833569519... | 705 | 28.2 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758479 | [S1] ... small solver where we also substitute the full solver with a small search tree. [S2] Mm-hmm. [S1] So rather than fully solving a game, we use the same method to basically do a search. [S2] Mm-hmm. [S1] And the outcome of the search, basically a small solver, is what the, the, what is the target. | 17.8 | 2.924421 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758479.mp3 | [
5792,
11883,
12618,
9777,
6163,
4452,
6496,
12698,
6552,
3786,
8277,
10852,
8439,
10862,
10420,
8371,
7955,
8942,
5544,
5536,
3361,
2960,
8055,
8439,
5815,
8415,
5269,
676,
3374,
6007,
8084,
5460,
3675,
3749,
2867,
11765,
9141,
2837,
683,
... | [
0.05865269526839256,
-6.124532222747803,
0.05608291178941727,
-0.412931352853775,
0.4433797299861908,
-0.14908020198345184,
0.5093839764595032,
0.05766148120164871,
-0.3578290343284607,
-3.9921648502349854,
0.02870693802833557,
-0.27134159207344055,
-0.20437295734882355,
0.2972454130649566... | 445 | 17.8 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758480 | [S1] Maybe this has battery again. So during the inference, you, you, you make, you do these queries, you store them in this, in this buffer, and these now act as the root nodes for yet another search, which is exactly the same as the previous search, right? And so you, you sort of rely on the fact that this search procedure can give you a better output than the neural network itself, right? [S2] Yes. Yes. [S1] So, right, the, the, the query here | 27.56 | 3.140431 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758480.mp3 | [
9867,
4763,
1053,
6382,
10142,
6861,
5800,
4437,
7436,
3785,
10167,
1818,
2841,
3984,
4925,
2230,
840,
3625,
8427,
2487,
7157,
12286,
8971,
3375,
787,
331,
4878,
9924,
2471,
2116,
9947,
7519,
11635,
11496,
8211,
6362,
8810,
4766,
4790,
99... | [
0.7923789024353027,
-5.550041198730469,
0.027606667950749397,
0.07218842953443527,
0.48033931851387024,
0.03781472146511078,
0.2419911026954651,
-0.05441603809595108,
-0.0684705376625061,
-3.8365020751953125,
0.09331415593624115,
0.22631357610225677,
0.03045954369008541,
0.0114237563684582... | 689 | 27.56 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758481 | [S1] That's- [S2] That's, that's exactly- [S1] One would hope, though, that after a while, you know, if I do this again and again and again, at the end, I wouldn't even have to ask the neural network anymore. I, sorry, I wouldn't even have to do search anymore during inference. Is that something you- | 18.68 | 3.288989 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758481.mp3 | [
5704,
942,
6527,
6078,
11928,
9304,
5378,
914,
6060,
3995,
6873,
5976,
9946,
9270,
4140,
3621,
7291,
4190,
6326,
9404,
4244,
4187,
7260,
5977,
10737,
11555,
9043,
5592,
11612,
9364,
9708,
8164,
6555,
10739,
9131,
9435,
9506,
876,
8923,
64... | [
0.8008184432983398,
-5.787570476531982,
-0.0611325278878212,
-0.019659439101815224,
0.5070059895515442,
0.10248430073261261,
0.19144339859485626,
-0.0783533975481987,
0.0350622832775116,
-3.7344932556152344,
0.14348526298999786,
0.05675715208053589,
0.10968799144029617,
0.09621210396289825... | 467 | 18.68 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758483 | [S1] Um, yeah, so I think we're, we're, we're almost getting already to the sort of results, uh, weight. Would you, would you maybe summarize the results a little bit? I think if people are super interested, they, they may go into, uh- [S2] Okay. [S1] ... into the paper and into the tables, but maybe you can just summarize a little bit of the results. You, you compared against Alpha Zero in | 24.16 | 3.287836 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758483.mp3 | [
8809,
6248,
11495,
6647,
10678,
8558,
10333,
10806,
3677,
8737,
8353,
9746,
8307,
9992,
9802,
12456,
5145,
6193,
9870,
12547,
10651,
11701,
1982,
4385,
8162,
3032,
11125,
7859,
10341,
3286,
4706,
9762,
9249,
9761,
8237,
6526,
7513,
5992,
86... | [
0.8647300004959106,
-5.66499137878418,
-0.02568153478205204,
0.04067225381731987,
0.41001150012016296,
0.006983225233852863,
0.13845588266849518,
-0.037692584097385406,
-0.07274401932954788,
-3.7482709884643555,
0.24121645092964172,
0.21376483142375946,
0.035175975412130356,
0.079560078680... | 604 | 24.16 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758487 | [S1] Do, do, do you mean for a Slumbot or for Scotland Yard? Are we, are we still talking about poker? [S2] Oh, sorry. Yeah. Let's, let's talk about poker for a while. So the, the, the player of games here gains, what is this, seven millibit blinds per, per hand. [S1] Yeah. [S2] Over Slumbot. | 17.28 | 3.149203 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758487.mp3 | [
7395,
9889,
1043,
3474,
11595,
11586,
8532,
9042,
11602,
3868,
3549,
7157,
7378,
8684,
11043,
8483,
11484,
8924,
5916,
8996,
6802,
5683,
6197,
6761,
4705,
9266,
6313,
6776,
3669,
1086,
803,
715,
11585,
8156,
10987,
11044,
10980,
11045,
8477... | [
0.6516265273094177,
-5.895227909088135,
0.009245111607015133,
-0.08588244020938873,
0.6077544689178467,
-0.09641308337450027,
0.36244603991508484,
-0.013638182543218136,
0.09601058065891266,
-3.8351714611053467,
0.07469741255044937,
-0.026080967858433723,
-0.14910459518432617,
0.1630379110... | 432 | 17.28 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758489 | [S1] Yeah. Okay. So, and here on the right side, you have a Pym Bot, which is, uh, the, a Scotland Yard-specific bot. For people, maybe people don't, does anyone not know what Scotland Yard is? Um, maybe you can describe 10 seconds what Scotland Yard even is as a game. It's somewhere, right? [S2] Yeah, there's a figure maybe, right? [S1] There is this figure, right? | 26.56 | 3.34405 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758489.mp3 | [
8816,
5161,
6276,
10124,
10725,
957,
1259,
4195,
9314,
11883,
12443,
12385,
8315,
10523,
9848,
7393,
9258,
6168,
9306,
9881,
9843,
6728,
9314,
9369,
6688,
9434,
8355,
9288,
7225,
3136,
12411,
6802,
7225,
4641,
9481,
9390,
7989,
3186,
12195,... | [
0.7935275435447693,
-5.572835445404053,
0.12411379814147949,
0.04453504458069801,
0.48048269748687744,
-0.10273392498493195,
0.21431051194667816,
0.0018832404166460037,
0.014795036986470222,
-3.7506208419799805,
0.18295928835868835,
0.07713478058576584,
-0.05267247185111046,
0.053897190839... | 664 | 26.56 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758490 | [S1] Yeah, so this is, this would be, I guess the final win rate is here, uh, like at 55% or something like this, and that is with a huge number of, of iterations for, for PIMBot. [S2] Yes, and the player games is using only like four, 400, uh, iterations on our side. [S1] Yeah. [S2] So, yeah, as you can see, as you can see, the regardless of the scale, we, we converge to a better policy. | 24.16 | 3.404915 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758490.mp3 | [
4641,
6264,
4226,
10052,
11204,
11190,
957,
4909,
4331,
4778,
5929,
849,
989,
10870,
8407,
8728,
5632,
8622,
12700,
736,
865,
201,
3421,
12198,
11622,
1362,
3353,
784,
4225,
5673,
3200,
12142,
12125,
3880,
841,
3746,
5302,
11038,
3619,
57... | [
0.5384827256202698,
-5.791027069091797,
-0.028330879285931587,
-0.2639510929584503,
0.6742650866508484,
-0.13610394299030304,
0.3484649062156677,
0.017151426523923874,
-0.16222161054611206,
-3.872487783432007,
-0.06133681908249855,
0.03603262081742287,
-0.06012289226055145,
0.1543561071157... | 604 | 24.16 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758491 | [S1] ... that we, we can get you an optimal policy. Uh, other methods that are based on MCTS, they, they are not guaranteed to converge even on small games. [S2] Mm-hmm. [S1] So there's, there's, there's, there's also the limit of the, of the fact that these methods are not sound. | 16.48 | 3.088853 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758491.mp3 | [
9654,
11280,
8793,
4361,
3164,
1195,
3423,
9413,
7493,
4486,
6933,
6291,
3684,
3365,
12614,
4747,
11873,
6637,
10023,
7710,
6754,
9562,
12783,
4527,
5712,
12176,
3366,
5990,
6686,
6739,
11517,
3255,
5217,
6313,
8530,
7463,
7781,
3327,
8731,... | [
0.07886457443237305,
-6.1377668380737305,
-0.0014662544708698988,
-0.36973148584365845,
0.5468883514404297,
-0.1706390380859375,
0.5816513299942017,
0.0765233039855957,
-0.2312539666891098,
-4.021181583404541,
0.061468906700611115,
-0.22643156349658966,
-0.16595059633255005,
0.272782802581... | 412 | 16.48 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758492 | [S1] I see. So I think the easiest to, for us, was, uh, poker. [S2] Mm-hmm. [S1] That, like, people probably can train on a few, few GPUs. Uh, the, the by far the hardest, uh, is, uh, is Go, where we used, uh, a, a lot of, uh, a lot of TPUs, but that was simply because we, we had them, uh, available. | 24.8 | 2.994558 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758492.mp3 | [
5672,
8728,
8812,
11262,
12748,
5009,
5520,
4048,
12678,
7557,
8849,
6370,
9379,
6684,
4134,
4126,
5221,
7197,
9247,
4188,
6257,
4307,
7593,
11216,
3024,
11052,
10986,
10396,
10453,
6291,
8353,
9882,
11930,
12443,
12450,
6257,
9313,
8813,
7... | [
0.12942062318325043,
-5.924623012542725,
0.06003768742084503,
-0.3600447475910187,
0.423245906829834,
-0.054726727306842804,
0.47788262367248535,
0.016716040670871735,
-0.18660074472427368,
-3.9063918590545654,
0.14512431621551514,
-0.1722191870212555,
-0.21258500218391418,
0.3432011306285... | 620 | 24.8 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758494 | [S1] You know, no human, no, like, no, not all humans together even will ever match Alpha Zero in, in any of these games, which is crazy. [S2] Yeah, you, you would not win a single game out of a thousand. [S1] Yeah, exactly. Um, do you, you have a bit of a demonstration ready, you told me, of, uh- [S2] Right. [S1] ... of, of, uh, the player of games playing Scotland Yard, so we can kinda see what's going on. | 27.16 | 3.141121 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758494.mp3 | [
7337,
6746,
4619,
10118,
3724,
5030,
4340,
670,
668,
6379,
8753,
7234,
4645,
4708,
6744,
4622,
8718,
10892,
11253,
5427,
7914,
7822,
4374,
10088,
7556,
11142,
10460,
10971,
8853,
10340,
11254,
6871,
3086,
5637,
1610,
4684,
8269,
8685,
10547... | [
0.8088822364807129,
-5.557581424713135,
0.07069861143827438,
-0.05184819921851158,
0.518688440322876,
0.015342357568442822,
0.19113001227378845,
-0.08992741256952286,
-0.05843024700880051,
-3.83663272857666,
0.17919795215129852,
0.04671986773610115,
0.09885726124048233,
0.02634111046791076... | 679 | 27.16 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758495 | [S1] ... color, details. So right now, I'm in here, and say I want to go to 49, and I want to use taxi to get there. So, yeah, hopefully, like we have been talking for a while, so maybe, maybe it's not, uh, alive, uh, anymore. But, uh, yeah, probably, probably it, uh, it died, uh, it died. [S2] You have scaled to zero, pr- proper engineering. [S1] [LAUGHS] [S2] Nice. | 26.52 | 3.007138 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758495.mp3 | [
5152,
9834,
12194,
11197,
11196,
10549,
7909,
8622,
11053,
8156,
3405,
2571,
8010,
11733,
9671,
8905,
11344,
6636,
8573,
6006,
6916,
10116,
3406,
661,
1749,
7136,
6024,
3520,
3992,
10025,
8930,
9889,
9833,
5152,
3609,
9931,
7330,
3602,
3971... | [
0.4854830801486969,
-5.77764892578125,
0.045557454228401184,
-0.1976032555103302,
0.3517671227455139,
-0.033793605864048004,
0.39570799469947815,
-0.026476843282580376,
-0.16026295721530914,
-4.013639450073242,
0.1912401020526886,
-0.21041753888130188,
-0.21932384371757507,
0.2058086246252... | 663 | 26.52 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758496 | [S1] Uh, we did. It's, I don't think it's, it's turn on right now, but it's exactly what we tried to do at some, at some point. [S2] Yeah. And did you see, did you observe this? Does the longer they didn't see Mr. X, the more kind of spread out, the more unsure they become? Is that something you can clearly observe or is that something you just feel as a human? [S1] Uh, yes. And it, it, it was actually really, really fun to, to see. [S2] Yeah. [S1] See, see that. | 28.44 | 3.01169 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758496.mp3 | [
3113,
5202,
7205,
9454,
11038,
8476,
7907,
11107,
5725,
803,
3563,
11585,
11971,
522,
5442,
11742,
11222,
7006,
8394,
9170,
7331,
8878,
11733,
9923,
8273,
4057,
3424,
856,
4385,
3968,
4896,
9377,
8949,
8702,
11733,
9795,
8082,
10544,
5794,
... | [
0.557623565196991,
-5.705898284912109,
0.10360894352197647,
-0.35713663697242737,
0.547706127166748,
0.022433968260884285,
0.43861085176467896,
0.053009964525699615,
-0.06325147300958633,
-3.9181275367736816,
0.13343507051467896,
-0.05346952751278877,
-0.10590612888336182,
0.23541273176670... | 711 | 28.44 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758497 | [S1] alleviated this, uh, by sort of going to the latent space state and training everything in latent space. Is this something I could do with player of games? [S2] Uh, no, but that's, uh, that's arguably the limitation, uh, number two. I think the biggest- [S1] Yeah. [S2] ... being, uh, the biggest thing is, uh, right now the, uh, the large, uh, large belief, uh, belief space. [S1] Mm-hmm. [S2] But the sec- second one is we currently need the model of the environment. | 28.44 | 3.378782 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758497.mp3 | [
9257,
5673,
11054,
6647,
5781,
2772,
3212,
5963,
12102,
12549,
12675,
3867,
1003,
12549,
12548,
1951,
4957,
4941,
5005,
7297,
1878,
1831,
7307,
8289,
7937,
3920,
4906,
9762,
9761,
6177,
4115,
1040,
3028,
5023,
1955,
1694,
4008,
4064,
3488,
... | [
0.48860248923301697,
-5.790985584259033,
0.05046960338950157,
-0.27827855944633484,
0.6158825755119324,
-0.07995403558015823,
0.3500349223613739,
-0.036542389541864395,
-0.2286900281906128,
-3.963975191116333,
0.0959247350692749,
-0.07994077354669571,
-0.14854788780212402,
0.17556807398796... | 711 | 28.44 |
bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758498 | [S1] So when you expand the search tree, do you need to expand once for every possible, let's say, flop combination there is? [S2] Yes. [S1] Okay. That, that is a lot of combinations, right? [S2] Or you can, or you can substitute, uh, like if you are smart about it, you, you can, again, it's a neural network. | 19.92 | 3.151267 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p162_BV17h4y1j7aZ_p162_m4-dialogue_0758498.mp3 | [
9256,
729,
2952,
10677,
7788,
2637,
2145,
6683,
627,
11693,
12274,
11789,
4292,
12482,
10389,
2694,
7210,
12134,
4885,
10794,
9692,
6120,
8160,
6089,
6249,
10602,
12798,
12726,
12212,
11644,
9252,
9295,
7171,
2752,
3439,
1961,
7977,
6016,
3... | [
0.49039727449417114,
-5.779314994812012,
-0.013721353374421597,
-0.14435063302516937,
0.5896185636520386,
-0.08040274679660797,
0.3361131250858307,
0.015078662894666195,
-0.1620132178068161,
-3.8541462421417236,
0.1130111813545227,
-0.06531602889299393,
-0.09758967161178589,
0.147099897265... | 498 | 19.92 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652852 | [S1] I'm not a German either, but, but I, I think that, uh, the author was called, was called Noether. Yeah, she- [S2] Yeah, yeah. So you're pronouncing it more properly than him, I think. [S1] [LAUGHS] Maybe. But essentially, could you give us maybe just first an insight, where does the name, because the name is kind of distinct, right? Because there is the Noether Theorem. [S2] Yeah. [S1] Uh, what does the Noether Theorem say in general? | 24.72 | 3.124485 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652852.mp3 | [
3689,
6296,
4654,
6398,
8635,
11875,
10340,
5779,
7988,
10484,
9281,
423,
9591,
6723,
6977,
10114,
4046,
5935,
8028,
8074,
8019,
8228,
10613,
11430,
7949,
9079,
11062,
11134,
12244,
9358,
5472,
11637,
7970,
5443,
5509,
3113,
8041,
6007,
593... | [
0.7102078795433044,
-5.63679838180542,
-0.01309791300445795,
-0.03228331357240677,
0.5072011947631836,
0.10106344521045685,
0.19802667200565338,
-0.023580363020300865,
-0.09588833153247833,
-3.8076815605163574,
0.18608111143112183,
0.08436817675828934,
-0.002597721992060542,
-0.00157935579... | 618 | 24.72 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652853 | [S1] of the theorem, uh, to, uh, build a new, uh, machine learning model. And the intuition is that in machine learning, symmetries are one of the core ways in which we've improved, uh, data efficiency and, and, and model performance. And so it would be very cool if we could kind of automatically learn some of these symmetries. [S2] Mm-hmm. [S1] Um, but, uh, symmetries are kind of hard to quantify and, and, and get a hold of, uh, computationally. And the intuition is that, um, | 27.08 | 3.268367 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652853.mp3 | [
2664,
11857,
8429,
3117,
3856,
821,
9440,
3160,
4442,
11082,
10611,
6084,
8669,
11685,
7717,
8867,
11937,
9889,
6693,
6702,
7724,
6189,
738,
3680,
4253,
6014,
3902,
6014,
8126,
3510,
10030,
6282,
5777,
11818,
8361,
5153,
9419,
9960,
3120,
... | [
0.6135091185569763,
-5.736812591552734,
0.0519380047917366,
-0.19224300980567932,
0.7219439744949341,
-0.10584337264299393,
0.2500820755958557,
0.1610729992389679,
-0.13939645886421204,
-3.9237163066864014,
0.429939866065979,
-0.21105913817882538,
-0.28134557604789734,
0.049552276730537415... | 677 | 27.08 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652854 | [S1] Yeah, we, we've heard in, I think in the recent past even, a lot of people attempting to get more out of symmetries out of neural network with, I'm thinking of, I'm thinking of like, uh, group convolutional neural networks and so on, that try to actively build in symmetries into neural networks. [S2] Yeah. [S1] But, uh, it seems like they can only do that in situations where they know | 23.8 | 3.387855 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652854.mp3 | [
6185,
5169,
3650,
7300,
9695,
12222,
2716,
5365,
11657,
8973,
617,
9257,
2731,
8540,
11650,
6420,
3568,
2993,
1960,
6698,
6311,
4971,
6964,
10539,
8042,
8009,
5442,
6146,
11328,
981,
6691,
9769,
9967,
11093,
11604,
10836,
10901,
11413,
8341... | [
0.7339105606079102,
-5.647878646850586,
0.06714997440576553,
0.01792934723198414,
0.44898587465286255,
0.013778088614344597,
0.1322716623544693,
-0.03175373747944832,
-0.10255549848079681,
-3.7156288623809814,
0.2842426002025604,
-0.0152367502450943,
-0.02499213255941868,
0.064112715423107... | 595 | 23.8 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652855 | [S1] ... the symmetry that will appear. They already know a molecule. It doesn't matter which way I look at it, right? So I can directly build that in. But your reasoning is that because assessing conserved quantities, um, is an easier task than assessing symmetries, it might be possible to learn the conserved quantities, uh, dynamically, actually learn them from data. [S2] Yeah. [S1] Is that approximately correct? | 25.4 | 3.406004 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652855.mp3 | [
4657,
5140,
2817,
9126,
6129,
8168,
6081,
11230,
12181,
5670,
7663,
4878,
7784,
9674,
2472,
2957,
8983,
12614,
3347,
12286,
4533,
1064,
3617,
174,
4015,
3165,
853,
1342,
3113,
8288,
10226,
10067,
12548,
12166,
9071,
2981,
395,
798,
1232,
... | [
0.7234684824943542,
-5.603499412536621,
0.10224258154630661,
-0.08476816117763519,
0.39385369420051575,
0.07033456116914749,
0.07626157253980637,
-0.008188603445887566,
-0.0856674388051033,
-3.7438881397247314,
0.2886233925819397,
-0.030408453196287155,
0.10975515842437744,
0.0629710480570... | 635 | 25.4 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652856 | [S1] Yeah. [S2] Oh, right. [S1] But that, that's a, another thing that may be a bit different, uh, from our work than other works, which is that, um, some symmetries are only approximately conserved or conserved quantities are only approximately conserved. So for instance, you ha- if you have a, a dissipative system, like, uh, in, in the real world, there's friction, and so, uh, you actually lose energy. If you don't cons- if you don't consider entire system, uh, you'll, you usually have small losses. | 26.8 | 3.236369 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652856.mp3 | [
4649,
8728,
7253,
10061,
11213,
9135,
8613,
2934,
2805,
4257,
3314,
1478,
3510,
1397,
2395,
1794,
7252,
3220,
8565,
4106,
3072,
10740,
11636,
9052,
3233,
3291,
3349,
6242,
8866,
4107,
3339,
11261,
11933,
10305,
450,
2845,
11045,
9063,
6503,... | [
0.5145800113677979,
-5.767024517059326,
0.07315463572740555,
-0.2554255723953247,
0.7461598515510559,
-0.14697541296482086,
0.3566681444644928,
0.1082453578710556,
-0.23720677196979523,
-3.967229127883911,
0.30849117040634155,
-0.23702317476272583,
-0.33699271082878113,
0.09816114604473114... | 670 | 26.8 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652857 | [S1] And maybe I wanna, I wanna get to sort of a little bit of an example of where, so people can imagine this a little bit more. Now, I only have a mouse here 'cause I forgot the iPad 'cause I'm, I'm stupid, but maybe we can give the small example of a, a pendulum, right? [S2] Mm-hmm. [S1] So here's a pendulum. It hangs here and it sort of | 20.52 | 3.413876 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652857.mp3 | [
6184,
7777,
12078,
12212,
8781,
4445,
6249,
8732,
1566,
10539,
11752,
12491,
7976,
12105,
11659,
7332,
9780,
11262,
9381,
5365,
10486,
11062,
10956,
9135,
6575,
4078,
7337,
9258,
5863,
7923,
11869,
8094,
6171,
12434,
9190,
11261,
10019,
11416... | [
0.7812725305557251,
-5.752928733825684,
-0.045062415301799774,
-0.020360548049211502,
0.5565292239189148,
-0.0051424941048026085,
0.31701356172561646,
-0.050412170588970184,
-0.08925320953130722,
-3.732182264328003,
0.2742457687854767,
-0.010137817822396755,
0.06513787806034088,
0.03976831... | 513 | 20.52 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652858 | [S1] ... the future, let's say. [S2] Yeah. [S1] Or at least from, from what I can tell. So what your model would be able to do is it would be able to predict the next time step right here, right? Then it's a bit here, here, uh, sorry. It's a, it's a little bit more up to the left, right? [S2] Mm-hmm. [S1] So it's a little bit more up and then it's, it's even more up over here and then it swings back and so on. It swings back over. Now, | 23.96 | 3.397678 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652858.mp3 | [
5672,
7713,
9231,
3075,
5385,
11182,
4514,
6448,
4025,
7379,
10053,
5327,
5254,
4819,
10833,
12755,
10200,
2990,
3046,
403,
322,
3731,
4786,
2205,
2845,
6135,
4717,
8400,
5473,
5465,
393,
9143,
4084,
5106,
2499,
4939,
8802,
10030,
10036,
... | [
0.8467458486557007,
-5.626096725463867,
-0.033399783074855804,
-0.06039230525493622,
0.4727981388568878,
0.04274888336658478,
0.2260766625404358,
-0.003642312018200755,
-0.12002212554216385,
-3.7415003776550293,
0.30607274174690247,
0.028208177536725998,
0.038150854408741,
0.02667573094367... | 599 | 23.96 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652859 | [S1] It will not actually make use of the fact that something is conserved. [S2] Exactly. [S1] So you, you go ahead and you say, "Since this is a dynamical system, we know more about the system. We can impose additional constraints." And the additional constraints right here, if I see this correctly, essentially at every time step you say, | 19 | 3.446182 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652859.mp3 | [
6769,
3084,
12526,
12245,
602,
2733,
11565,
5332,
8780,
10557,
10418,
4270,
4136,
8815,
8189,
10036,
11555,
12762,
3984,
7965,
12612,
6157,
10916,
12790,
12181,
7308,
11298,
7578,
6796,
7434,
9605,
10451,
7756,
2566,
199,
5019,
8672,
2970,
... | [
0.7644492387771606,
-5.7555670738220215,
-0.04248243570327759,
-0.016025247052311897,
0.3863508403301239,
0.07892829179763794,
0.1953110694885254,
-0.03826412558555603,
-0.15935736894607544,
-3.7887473106384277,
0.26067420840263367,
0.019033467397093773,
0.02837173454463482,
0.038677737116... | 475 | 19 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652860 | [S1] And it always needs to predict the same thing, right? Since, since it needs to, uh, figure out a quantity that is conserved. [S2] Exactly. [S1] And now it is, it is, if I just train a neural network to always predict the same number right here, I would just end up with a neural network that is predicting some, some kind of a constant, right? [S2] Yeah. [S1] So your method figures out how do I need to build | 28.4 | 3.270911 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652860.mp3 | [
5672,
3633,
11300,
11773,
12061,
9282,
9119,
9931,
3036,
7931,
10418,
3170,
10557,
12180,
6552,
651,
10901,
12683,
12492,
5905,
2897,
3265,
4060,
3204,
10930,
4016,
3037,
3853,
9033,
11182,
12189,
9258,
12179,
4121,
194,
5046,
8681,
9689,
6... | [
0.7935916781425476,
-5.611802101135254,
0.016887884587049484,
0.00680740037932992,
0.3808141350746155,
0.017207946628332138,
0.19656731188297272,
-0.03630748391151428,
-0.1571626514196396,
-3.7609992027282715,
0.24260857701301575,
0.08283078670501709,
0.028543855994939804,
0.07491514831781... | 710 | 28.4 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652861 | [S1] Um, so this is the physics 101 data set, um, from Josh Tenenbaum's group. I think Jesuine was the first author. And they have a collection of videos, and in this case it's a, they have a hand dropping an object passively, like it, it just lets it drop down, and the object falls down, and there's a second object at the end of the ramp. They collide, and then, uh, the other one, sometimes depending on the masses and the friction and whatnot, uh, the dynamics are kind of, can, can change. [S2] Mm-hmm. [S1] Uh, but that's the data set. | 26.48 | 3.312711 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652861.mp3 | [
11604,
6809,
9829,
7284,
10069,
8882,
9865,
4839,
11766,
10670,
11190,
10741,
8190,
6886,
8403,
3293,
6371,
6880,
2824,
8116,
10542,
6725,
2696,
12188,
11587,
3025,
11091,
5961,
3204,
2835,
11188,
11686,
6496,
5432,
9180,
11652,
3008,
6092,
... | [
0.5998235940933228,
-5.807607650756836,
-0.08215015381574631,
-0.02447568066418171,
0.748042106628418,
-0.04305308312177658,
0.3246605396270752,
0.08104247599840164,
-0.15597166121006012,
-3.897087335586548,
0.31464555859565735,
-0.20380322635173798,
-0.21485930681228638,
-0.00103789870627... | 662 | 26.48 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652862 | [S1] And does, so that there are multiple videos- [S2] Yes. [S1] ... and it's always different objects or- [S2] Uh, like some objects could be, uh, common between, uh, videos, but- [S1] Mm-hmm. [S2] ... there's lots of objects, so, uh, it's not always the same object and that's- [S1] Yeah. [S2] ... can point to the fact that it can vary. Um, so- [S1] Mm-hmm. [S2] ... one nice thing about the, in other networks is that, um, they can deal with, uh, with raw video. So some, usually conserve quantities, | 27.08 | 3.275151 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652862.mp3 | [
8777,
10139,
7284,
3764,
4962,
6769,
10989,
11260,
12203,
10315,
11406,
8715,
8149,
4445,
8801,
9762,
6805,
9755,
11865,
6193,
8281,
10496,
11221,
10677,
7957,
5577,
5570,
3010,
6866,
9250,
9242,
11298,
5154,
8348,
7204,
4379,
10712,
5504,
... | [
0.6509643793106079,
-5.726364612579346,
-0.018672071397304535,
-0.0627375990152359,
0.591183066368103,
-0.032542627304792404,
0.30112096667289734,
0.05806456133723259,
-0.19579486548900604,
-3.8861513137817383,
0.22996042668819427,
-0.07245976477861404,
-0.16312433779239655,
0.012229015119... | 677 | 27.08 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652863 | [S1] Yeah. So here, the, the, the diagram shows a little bit of, of what you're, what you, um, are trying to do, but also what you're trying to avoid. So the bottom path right here, if I see this correctly, that would be if I did nothing else except the bottom path, I would build this neural network to just predict sort of the, the, the future time steps. And that often turns out poorly. [S2] Mm-hmm. [S1] I don't know. This is a, quite a pixel-ish, | 29.08 | 3.469985 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652863.mp3 | [
7274,
9265,
1603,
10117,
10742,
1836,
553,
3741,
11946,
9306,
11932,
6756,
12403,
6747,
6813,
4710,
7211,
4134,
4206,
7205,
4142,
2597,
2076,
1626,
6177,
5737,
5145,
9793,
4681,
3731,
7849,
3609,
8833,
7217,
4674,
6705,
4629,
722,
6112,
3... | [
0.8140379786491394,
-5.603418350219727,
-0.007100473158061504,
0.008310975506901741,
0.4742708206176758,
0.0031012094113975763,
0.19011451303958893,
-0.08267756551504135,
-0.13734076917171478,
-3.7368741035461426,
0.27166295051574707,
0.019436826929450035,
0.10032743215560913,
0.0496099404... | 727 | 29.08 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652864 | [S1] Um, mess, but it, it, it sort of, it sort of, all of a sudden there are, like, three objects instead of two, and th- the one is, the one is kind of gone or split up. [S2] Yeah. [S1] And it's a, it's a bit of a, a mess. And you attribute this to the fact that it's just a video prediction or- | 19 | 3.301306 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652864.mp3 | [
4129,
7713,
12391,
9199,
4079,
3575,
3566,
3575,
4015,
6221,
5678,
3110,
11303,
7387,
11305,
8360,
7729,
2052,
5718,
11294,
11365,
8742,
10908,
12775,
12790,
11755,
12652,
7666,
3552,
3474,
2961,
3465,
464,
7322,
12517,
9974,
4394,
7184,
53... | [
0.8282726407051086,
-5.7625956535339355,
-0.1088097095489502,
-0.04559221863746643,
0.3750263750553131,
0.06801831722259521,
0.267947793006897,
-0.039654675871133804,
-0.0733712837100029,
-3.7556073665618896,
0.2285650670528412,
0.11248553544282913,
0.08990537375211716,
0.0297853983938694,... | 475 | 19 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652865 | [S1] At the very few, like at the beginning of the frames, like the first few frames, there was not that much mistakes, but when you go very far into the future, then it's, it's much harder. Um, so- [S2] Yeah. [S1] ... those two problems, lack of data and the fact that you go a lot of, into the future. [S2] Your method is, and you also have an algorithm described somewhere. It's a bit of a, it's a, it's a algorithm that is, oh, right here. It's an algorithm that has multiple steps in it, and one special part is that you have this sort of inner optimization loop right here. [S1] Yeah. [S2] Now, | 29.8 | 2.94873 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652865.mp3 | [
4203,
8282,
6884,
5502,
9974,
5121,
3216,
7437,
9414,
9990,
7557,
4363,
9380,
1181,
3877,
3805,
3155,
3107,
6177,
6185,
3594,
3594,
3155,
1371,
12781,
9069,
341,
8590,
10124,
3440,
7593,
9987,
11020,
5206,
1759,
12654,
8862,
5541,
10094,
... | [
0.670535147190094,
-5.663235664367676,
0.008839651942253113,
0.011367485858500004,
0.5341405868530273,
0.04107864573597908,
0.20862184464931488,
-0.023860398679971695,
-0.16035950183868408,
-3.809791088104248,
0.2830492854118347,
0.03774743154644966,
0.03392321988940239,
0.0837969407439231... | 745 | 29.8 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652866 | [S1] I wanna maybe go back to the diagram and let's go, let's walk through it once. Before we, before we, you know, take a look at the formulas and all, we can walk through it once. So the first thing that happens, if I understand correctly, is you take your first input and you do exactly what we just said. You run it through a forward prediction neural network that just tries to predict the future, um, just plain by itself. [S2] Yeah. [S1] Right. | 27.72 | 3.344877 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652866.mp3 | [
5744,
11299,
12222,
8125,
9446,
2805,
7925,
12022,
8453,
9071,
12143,
9127,
8687,
2469,
6828,
11314,
6705,
2062,
8421,
12267,
12163,
7776,
4516,
12549,
10054,
7558,
9542,
4420,
602,
1401,
6819,
6258,
11987,
5935,
10470,
1058,
10984,
11767,
... | [
0.7751098871231079,
-5.5993523597717285,
0.008594769053161144,
-0.00978932436555624,
0.36663269996643066,
0.04201892018318176,
0.1823028326034546,
-0.044867321848869324,
-0.17145252227783203,
-3.7749855518341064,
0.3244750201702118,
0.029104329645633698,
0.09248568117618561,
0.093528315424... | 693 | 27.72 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652867 | [S1] So this has, this has a bit of a, of a default thing. But now you try to improve that. And this is all, this is the entire thing we're describing right now. That is one forward pass through your system. [S2] Mm-hmm. [S1] So ev- you would take every single prediction that you made and you would feed it through this G-network right here. And this G-network is, you call it an embedding network. [S2] Mm-hmm. [S1] That is the thing ultimately that's trying to predict a conserved quantity. | 28.96 | 3.486659 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652867.mp3 | [
2088,
8849,
1232,
860,
1629,
2560,
6116,
12629,
5601,
5569,
7668,
9727,
8567,
3472,
2961,
329,
6169,
4137,
2825,
12268,
3427,
5513,
3554,
12222,
11198,
6485,
458,
9135,
4122,
10665,
11749,
7245,
5580,
10551,
3491,
6128,
3502,
4015,
1462,
... | [
0.7404557466506958,
-5.631862163543701,
0.03038349375128746,
0.022720934823155403,
0.44495266675949097,
0.06365137547254562,
0.15796950459480286,
-0.04675755277276039,
-0.14977319538593292,
-3.754385471343994,
0.2632155120372772,
0.03537017107009888,
0.047918953001499176,
0.073940545320510... | 724 | 28.96 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652868 | [S1] But it's not, it's not necessarily just outputting one number. It's outputting an entire vector. [S2] Mm-hmm, mm-hmm, yeah. [S1] So it's an, outputting an embedding vector and the, the goal obviously is that for all of these inputs, it should output the same embedding vector. | 15.8 | 3.217804 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652868.mp3 | [
6184,
7729,
10985,
8639,
12382,
462,
12188,
9810,
5523,
5592,
3331,
10388,
7990,
10868,
10620,
9889,
5664,
2953,
11297,
9258,
8749,
12757,
9745,
2898,
714,
10460,
7994,
11382,
1568,
4163,
10964,
12221,
2912,
5568,
3813,
5600,
6611,
11195,
5... | [
0.796582818031311,
-5.8233723640441895,
-0.140767902135849,
0.0009273095056414604,
0.4723527133464813,
0.08299762010574341,
0.21464170515537262,
-0.07483884692192078,
-0.09227418899536133,
-3.7269985675811768,
0.2423694133758545,
0.0036838860251009464,
-0.01649666018784046,
0.0425464510917... | 395 | 15.8 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652869 | [S1] Exactly. [S2] But all the same across the, across the, uh, video sequence, okay? So this is how we can imagine you train this G-network to sort of predict whatever is special about this particular data point, but inside of the data point, conserved among all the frames. [S1] Exactly. Because if it was the same A for everyone, then you would have the issue that you mentioned at the beginning, then it's a useless conserved point. | 25.44 | 3.396624 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652869.mp3 | [
11938,
6354,
8876,
9877,
7572,
3019,
8638,
4901,
9947,
2204,
10593,
8063,
6391,
10944,
4452,
8233,
10870,
2806,
3177,
8746,
6193,
11375,
8502,
10421,
10421,
11059,
7957,
3777,
3447,
1435,
10721,
8657,
3539,
11764,
11749,
2436,
2445,
5669,
6... | [
0.7086426615715027,
-5.662154674530029,
0.014759296551346779,
-0.09785746783018112,
0.5294415950775146,
0.029721437022089958,
0.1695239245891571,
0.007338250055909157,
-0.2629934847354889,
-3.8269009590148926,
0.3107975721359253,
-0.004091132432222366,
0.024529557675123215,
0.0559459961950... | 636 | 25.44 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652870 | [S1] Yeah. Um- [S2] It, it restricts, it restricts what you can output, right? Because ideally, the F-network should only output whatever the G-network says is, is the same, right? [S1] Yeah. [S2] If the F-network can only output things that the G-network will embed to the same place in the embedding space or a similar place. | 21.16 | 3.433089 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652870.mp3 | [
3793,
1747,
7061,
1341,
1128,
6697,
8754,
7891,
6761,
5161,
9811,
7021,
3886,
3887,
3951,
1391,
1334,
3822,
6237,
6819,
11741,
9858,
11408,
5665,
8962,
8745,
7204,
11677,
11156,
2258,
3741,
2844,
5524,
7004,
9192,
5601,
3336,
3785,
9618,
... | [
0.823232889175415,
-5.681219577789307,
-0.03168993070721626,
-0.006517555098980665,
0.5076174736022949,
0.03293566778302193,
0.14903941750526428,
-0.02575136534869671,
-0.13338777422904968,
-3.7200796604156494,
0.3030100464820862,
0.028910284861922264,
0.07075834274291992,
0.04021979868412... | 529 | 21.16 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652871 | [S1] ... of this information that the G-network output out of the initial sequence again. And you do this in a very special way in that you actually take the parameters of F and you update them on the fly. [S2] Yes. [S1] Uh, you update them on the... So this is within a forward pass. You actually update the parameters | 19.36 | 3.43071 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652871.mp3 | [
6192,
7712,
11503,
5943,
3190,
5879,
5221,
489,
5552,
1834,
5760,
10151,
10071,
1427,
5513,
2962,
5593,
457,
3778,
11825,
5225,
12450,
6769,
8218,
12710,
12108,
10766,
3931,
6009,
443,
5525,
5782,
3118,
10972,
11767,
12181,
12676,
5082,
970... | [
0.721305787563324,
-5.728343963623047,
-0.037755001336336136,
-0.044394638389348984,
0.4791141450405121,
0.08710592240095139,
0.17870290577411652,
-0.06644228100776672,
-0.1278018057346344,
-3.764582872390747,
0.2792128622531891,
-0.05600711703300476,
0.10150863230228424,
0.066768683493137... | 484 | 19.36 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652872 | [S1] into the direction of the gradient of G. [S2] Exactly. Yes. So- [S1] So, yeah. Sorry. This is, I think that, that, it takes it, yeah. So here you have this neutralos. [S2] Yes, exactly. [S1] Which you maybe want to talk about this briefly. | 17.72 | 3.271819 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652872.mp3 | [
6193,
7712,
11365,
11723,
11585,
11277,
12371,
5225,
12233,
6571,
10957,
3206,
8256,
9695,
4742,
8577,
10087,
6438,
788,
845,
9647,
11253,
7466,
8754,
12196,
9697,
10192,
3045,
8342,
8207,
6422,
8809,
8310,
7998,
5932,
4081,
8385,
3959,
925... | [
0.8298799991607666,
-5.770203113555908,
-0.0668395534157753,
-0.07657423615455627,
0.44547709822654724,
0.00853731483221054,
0.17481829226016998,
-0.06453493982553482,
-0.22571659088134766,
-3.7357938289642334,
0.28373637795448303,
-0.04596327617764473,
-0.00012848200276494026,
-0.01433231... | 443 | 17.72 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652873 | [S1] and now we update theta, and theta are the parameters of F, right? [S2] Exactly. [S1] Theta are the parameters of F. We update these on the fly, and I, I, I suppose that we just do this, you know, in the moment and for the next data point we, we go back to the sort of original parameters. [S2] Yes. [S1] Uh, and do this again. So this is sort of an on-the-fly update for, you know, a temporary update of these parameters into the direction | 25.92 | 3.239306 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652873.mp3 | [
3113,
7720,
11638,
7374,
5637,
8773,
8718,
11222,
12725,
12603,
10403,
2716,
2683,
11101,
12547,
5061,
6379,
8745,
8745,
5745,
11375,
5566,
10619,
9258,
10289,
5737,
9194,
9824,
3625,
8002,
11180,
9711,
2518,
5061,
7557,
7557,
11928,
6177,
... | [
0.7682434320449829,
-5.552507400512695,
0.08033601194620132,
-0.12870219349861145,
0.45626768469810486,
0.04527881368994713,
0.19887854158878326,
0.01992158405482769,
-0.09041404724121094,
-3.7750139236450195,
0.3482648432254791,
0.020271461457014084,
0.040589723736047745,
0.06202369183301... | 648 | 25.92 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652874 | [S1] Exactly, exactly. So this is, uh, some, uh, previous work of ours, uh, which we call tailoring. And the idea of- [S2] Mm-hmm. [S1] ... of tailoring is just because of what you said, that the fact that the adaptation is customized for each individual data point. [S2] Mm-hmm. [S1] Uh, and the idea there was, uh, a general way of encoding inductive biases, uh, with, uh, unsupervised auxiliary losses. So | 19.72 | 3.062619 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652874.mp3 | [
8744,
11622,
7309,
10012,
833,
3935,
9079,
7453,
11946,
1884,
3934,
7378,
3305,
6193,
5160,
12315,
4455,
12444,
1857,
3047,
4598,
7332,
10147,
3660,
1820,
9000,
5440,
11125,
10413,
6350,
3152,
3361,
12180,
5962,
3009,
10650,
11532,
4554,
58... | [
0.496900349855423,
-5.9109930992126465,
0.004077565390616655,
-0.20128045976161957,
0.7276714444160461,
-0.1661660224199295,
0.4107508957386017,
0.10646949708461761,
-0.12711673974990845,
-3.8591232299804688,
0.3728911876678467,
-0.25075677037239075,
-0.3427937626838684,
0.0831920430064201... | 493 | 19.72 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652875 | [S1] ... loss is in general, you say, for instance, one thing we could say is, "Oh, why not we add energy conservation when, when we train?" Sometimes, auxiliary losses would say, "Okay, I train for good predictions and I train for energy conservation at training time." But if you do that, you're not going to enforce energy conservation at test time. [S2] Yeah. [S1] Because at test time, you're going to have a generalization gap, uh, in energy conservation. | 21.08 | 3.318252 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652875.mp3 | [
4204,
4256,
4172,
10046,
3374,
6308,
4995,
3531,
11091,
5981,
5845,
5550,
10494,
6626,
8064,
9573,
5824,
979,
7183,
6212,
9600,
11247,
12524,
5908,
7981,
8502,
5762,
4875,
9541,
2839,
4955,
8592,
3036,
12775,
7621,
4192,
5497,
5405,
6557,
... | [
0.547906756401062,
-5.831568717956543,
-0.0893828272819519,
-0.13150782883167267,
0.8349735736846924,
-0.18422837555408478,
0.40756598114967346,
0.11023510992527008,
-0.15759624540805817,
-3.870570421218872,
0.36208376288414,
-0.2018779069185257,
-0.2628067433834076,
0.005724901333451271,
... | 527 | 21.08 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652876 | [S1] But- [S2] Yeah. [S1] Uh, energy, uh, conservation, or any type of conservation, or any auxiliary laws, can be checked before making the prediction at test time or at training time. Inside the prediction function, I can first make my prediction and see, okay, uh, do I like it? Does my auxiliary laws, does my unsupervised laws like this prediction? | 18.64 | 3.094048 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652876.mp3 | [
7186,
8224,
9301,
3618,
5620,
10673,
10675,
6365,
5776,
3088,
3473,
7474,
5792,
2068,
1057,
8026,
12758,
11923,
4084,
3212,
3617,
9266,
5160,
4810,
7721,
5665,
1683,
7493,
2998,
3900,
4266,
3681,
9243,
8913,
6369,
9843,
3666,
9819,
9647,
... | [
0.5740753412246704,
-5.960941791534424,
-0.14256669580936432,
-0.047546517103910446,
0.7567527890205383,
-0.1291540414094925,
0.39323049783706665,
0.05375494435429573,
-0.045079074800014496,
-3.8543436527252197,
0.3468329906463623,
-0.2539527714252472,
-0.3055499494075775,
0.00299502094276... | 466 | 18.64 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652877 | [S1] Yeah, maybe it's, it's also important to highlight that the, the, the parameter here, this theta that we start with, and also the parameters of G, those are the ones that will be learned during the training procedure across the entire training data set. And then the parameters here, those are always constructed in the moment, data point by data point- [S2] Exactly. [S1] ... to, | 24.2 | 3.378997 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652877.mp3 | [
6696,
5144,
4748,
12164,
10677,
1843,
1057,
4135,
11109,
11745,
7181,
8040,
12100,
10644,
11182,
9729,
2970,
5537,
3024,
6793,
6249,
9383,
11678,
12110,
5648,
3027,
464,
11631,
7799,
7918,
1954,
5513,
3044,
8431,
1438,
7446,
5668,
8297,
140... | [
0.7895370125770569,
-5.644230842590332,
-0.04177823290228844,
-0.0060137175023555756,
0.42354488372802734,
-0.0021596341393887997,
0.18649786710739136,
-0.07277274131774902,
-0.17062503099441528,
-3.722886085510254,
0.29839184880256653,
0.015071652829647064,
0.1124911978840828,
0.046819902... | 605 | 24.2 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652878 | [S1] ... As you say, tailor the inductive bias. And the inductive bias in this case would sort of be the, this entire term right here. [S2] Exactly. [S1] Essentially says, you know, what do, how do I need to change my predictor in order to conserve the particular thing that G decides is the common quantity for this data point. [S2] Yeah, yeah. | 20.36 | 3.449466 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652878.mp3 | [
6681,
10385,
10004,
7276,
12526,
12278,
8637,
6420,
960,
9861,
8966,
2514,
11753,
5584,
11774,
2454,
4997,
10848,
10768,
11226,
7666,
10226,
12772,
12675,
11668,
2708,
8109,
11107,
2893,
8525,
3280,
7014,
11971,
4055,
9246,
8513,
6455,
8951,
... | [
0.893925130367279,
-5.672552108764648,
-0.03166671097278595,
-0.014118524268269539,
0.4624546766281128,
-0.04575183615088463,
0.1679815948009491,
-0.04169321805238724,
-0.08559892326593399,
-3.7523529529571533,
0.21940895915031433,
-0.040457434952259064,
0.03477838262915611,
0.011410579085... | 509 | 20.36 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652879 | [S1] and the L task here, that would just be the, what do you call the task loss. This would be the video prediction loss. [S2] Exactly. [S1] Or something like this. Okay. So my, I have, I have, I have a lot of questions. Um, first, first of all, this, it seems, it seems quite, um, intricate, right? Because if I think, okay, these outer gradients right here, especially this gradient right here, this is, how do I need to change theta? Now, okay, | 28.16 | 3.311375 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652879.mp3 | [
2672,
4131,
8950,
2549,
2421,
4630,
9733,
5760,
1305,
3088,
1939,
9924,
4939,
5681,
10861,
12782,
12787,
10925,
5341,
5453,
7457,
8392,
6632,
5044,
10175,
9661,
8637,
9598,
8617,
5585,
4891,
10147,
7600,
4877,
9990,
12135,
8020,
2964,
3734,... | [
0.7504711151123047,
-5.548388481140137,
0.012188289314508438,
-0.03246091678738594,
0.564893901348114,
0.04579634591937065,
0.16643042862415314,
-0.03624039143323898,
-0.14696210622787476,
-3.7966809272766113,
0.19664233922958374,
0.003792242845520377,
0.1527264565229416,
0.050970606505870... | 704 | 28.16 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652880 | [S1] How do we need to change theta? This depends on these predictions right here. These predictions right here have one forward pass using theta, then have a gradient with respect to theta right here inside of them, right? [S2] Yeah. [S1] And, and the, all of those come from this quantity, which is already a forward pass using theta, right? Is, is this, | 24.56 | 3.491502 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652880.mp3 | [
4649,
7713,
6980,
7468,
9725,
8054,
9794,
2910,
7966,
11781,
12694,
9989,
10952,
1942,
8321,
12689,
6639,
5085,
9806,
7554,
5056,
1808,
5832,
8634,
11552,
12178,
8962,
8567,
9455,
2752,
11701,
7004,
8088,
1153,
5248,
12758,
6184,
6192,
7154... | [
0.7064188718795776,
-5.677851676940918,
-0.014043397270143032,
-0.039112769067287445,
0.4936772584915161,
0.11409427970647812,
0.24087347090244293,
-0.04528265818953514,
-0.044007617980241776,
-3.7510719299316406,
0.2546812891960144,
-0.009845473803579807,
0.11130528151988983,
0.0409582592... | 614 | 24.56 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652881 | [S1] Uh, is stable, then it works fine. Um, but if it, if the overall thing is already unstable, then it's ex- it's extremely tricky to, to, uh, add things there. So for instance, uh, one thing we realized was that, um, because video prediction is very expensive, uh, and, and basically we couldn't fit that many examples on a GPU, literally, I think, two or four. [S2] Right. [S1] Uh, so, um, we were initially using batch normalization. | 26.84 | 3.245497 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652881.mp3 | [
3624,
6658,
9902,
6591,
6007,
6063,
3503,
5998,
3438,
3958,
3958,
3446,
3446,
3965,
3884,
7467,
8233,
8289,
11300,
12613,
9988,
3916,
712,
2834,
5528,
2953,
4442,
9770,
8809,
5289,
5664,
7198,
5027,
5912,
3088,
3338,
12781,
11723,
6163,
5... | [
0.6157985925674438,
-5.811166763305664,
0.04294733703136444,
-0.11421522498130798,
0.6925292611122131,
-0.1748984009027481,
0.352013498544693,
0.08588189631700516,
-0.18237292766571045,
-3.95110821723938,
0.4015984535217285,
-0.2507287263870239,
-0.35030320286750793,
0.02351865917444229,
... | 671 | 26.84 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652882 | [S1] If the grad- of the, the original gradients, because of the batch normalization, if you compute the batch statistic with a very small batch, it's already very crazy and unstable. [S2] Mm-hmm. [S1] And then we could have it. When the or- the other thing is already stable, then it seems, um, for us, it worked pretty out of the box when we, when we swapped the linear normalization. [S2] Okay. That sounds good. Yeah, I would, I would expect so, um- | 22.08 | 3.207426 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652882.mp3 | [
6192,
9241,
12565,
11651,
5916,
3738,
2840,
9053,
11524,
12037,
12166,
12677,
4485,
6809,
11929,
11873,
11322,
9256,
8745,
6185,
5673,
6193,
12369,
8790,
5723,
11371,
804,
3532,
11230,
5012,
6298,
10085,
10021,
7919,
3291,
667,
5121,
5614,
... | [
0.7831315398216248,
-5.824700832366943,
-0.048473093658685684,
-0.1159050464630127,
0.6492183208465576,
-0.050268951803445816,
0.43590280413627625,
0.051971226930618286,
-0.11161254346370697,
-3.9607834815979004,
0.19581933319568634,
-0.13610436022281647,
-0.30627191066741943,
0.0105286445... | 552 | 22.08 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652883 | [S1] was when we were using batch normalization and we were wondering, "Oh, is- is our another network unstable?" Or- [S2] Yeah. [S1] And then we realized, okay, no, it's the- it's the- the vanilla network that was unstable. Um, but, uh, that was part of our concern because, uh, there is some papers that mention that when there's a- when you're back propagating through a very deep, um, graph, uh, then the gradients are sometimes not very informative. [S2] Mm-hmm. [S1] Um, in our case, we found that, uh, when the- our thing is pretty stable, | 27.24 | 3.025206 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652883.mp3 | [
4202,
9240,
4190,
3109,
5226,
5300,
5302,
3375,
8951,
8502,
11126,
11702,
6609,
5464,
5449,
4949,
8878,
2675,
5366,
12278,
9942,
5782,
2789,
12102,
165,
7990,
5525,
3853,
10051,
5959,
4033,
11669,
11988,
4638,
8189,
10166,
11585,
4032,
8853... | [
0.7478934526443481,
-5.684506416320801,
-0.013155665248632431,
-0.14443309605121613,
0.7434341907501221,
-0.15126082301139832,
0.322050005197525,
0.1281297206878662,
-0.04854438453912735,
-3.9509739875793457,
0.42470863461494446,
-0.22037771344184875,
-0.3357049822807312,
-0.08138347417116... | 681 | 27.24 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652885 | [S1] That, that, that might also be something where stability or computational graph size. First of all, you just do a gradient step. Many things would be possible, right? You could do an Ad- an Ad-grad step, you could do an Adam step, you could do a line search or a Newton step or anything like this, but you have chosen to do like the most simple thing, which is a single gradient step, right? [S2] I think, I think the, the- [S1] Yeah. | 26.32 | 3.392578 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652885.mp3 | [
5648,
5681,
4621,
5450,
11260,
10683,
4380,
3096,
1042,
2963,
11709,
12590,
5515,
9446,
1688,
4116,
3282,
10683,
7477,
33,
8807,
11126,
11637,
9998,
7768,
8754,
6176,
9240,
8503,
10429,
8934,
5537,
3537,
3892,
3166,
10416,
5015,
9030,
4931,... | [
0.7932926416397095,
-5.604663848876953,
0.02702830731868744,
-0.014306903816759586,
0.38908687233924866,
0.021092094480991364,
0.1211545467376709,
-0.039863090962171555,
-0.17423300445079803,
-3.7924094200134277,
0.34380316734313965,
0.00015696670743636787,
0.08745371550321579,
0.073340982... | 658 | 26.32 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652886 | [S1] Uh, when you force the conserve G, all these features say, okay, no, you should conserve G and, therefore, it's kind of, uh, projecting one dimension. And so, um, in particular for conserve quantities, applying the same laws over and over, uh, it's kind of stable because, uh, it, you will just keep going closer to this, uh, manifold of- [S2] Mm-hmm. [S1] ... predictions that conserve, uh, G. | 21.4 | 3.29015 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652886.mp3 | [
6184,
7712,
11998,
12269,
11630,
11759,
8686,
3365,
107,
5738,
8297,
8424,
9236,
2731,
11702,
12358,
9859,
9038,
5214,
1195,
5360,
2801,
10354,
8033,
6547,
1296,
7961,
8415,
12506,
5367,
11412,
6552,
3043,
5542,
3348,
682,
8913,
9153,
11089... | [
0.5323896408081055,
-5.891219139099121,
-0.08418269455432892,
-0.07803566753864288,
0.8425347208976746,
-0.09039291739463806,
0.44781380891799927,
0.15543779730796814,
-0.1104183942079544,
-3.890838384628296,
0.27585387229919434,
-0.14703437685966492,
-0.2515547573566437,
0.039770547300577... | 535 | 21.4 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652887 | [S1] So there's no, no, let's say, danger of over, overdoing. I mean, there's a little bit, but it, as I said, it hits after like 100 steps, which- [S2] Yeah, yeah. [S1] ... which is quite a bit, right? Given that you train with one. | 12.2 | 3.534303 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652887.mp3 | [
6827,
5681,
4428,
3024,
11118,
3787,
9206,
8021,
5534,
5001,
339,
8214,
6053,
10934,
10934,
10405,
7894,
10470,
7966,
6749,
9827,
11379,
9826,
9998,
7387,
9793,
10890,
10011,
7286,
3236,
7217,
3138,
6799,
10901,
10613,
10412,
10918,
7911,
6... | [
0.990776002407074,
-5.788281440734863,
-0.13488072156906128,
0.05750149488449097,
0.500278890132904,
-0.0030178504530340433,
0.2911088764667511,
-0.07028695195913315,
-0.13602083921432495,
-3.7283763885498047,
0.2363615483045578,
-0.04533426836133003,
-0.015723034739494324,
-0.007587935309... | 305 | 12.2 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652888 | [S1] But because it's a neural network, then suddenly I think you've tra- you're going, uh, outside. It's kind of a distribution shift. You train G to be useful for one or two or three- [S2] Yeah. [S1] ... green steps. Now you're using it for 100. It doesn't make you any promises. Um- [S2] Yeah. | 14.44 | 3.254836 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652888.mp3 | [
7266,
11273,
5727,
5668,
8937,
8631,
5494,
1015,
7535,
11344,
5192,
1755,
3619,
6498,
10117,
11929,
2934,
10550,
4544,
12573,
10946,
3026,
9463,
7693,
10006,
11614,
2901,
3502,
9318,
8100,
12715,
5273,
5427,
7955,
478,
9941,
11882,
2395,
30... | [
0.61126708984375,
-6.0702619552612305,
-0.09284675866365433,
0.016791410744190216,
0.6812424659729004,
-0.09810420125722885,
0.4329046905040741,
0.07113566994667053,
-0.20854023098945618,
-3.883117437362671,
0.19127848744392395,
-0.1606038212776184,
-0.2587840259075165,
0.05229330062866211... | 361 | 14.44 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652889 | [S1] ... forward predicts. For example, you could have optimized the predictions themselves at runtime- [S2] Yes. [S1] ... uh, to make both of them happy. You could have, um, I, I don't know, you could have, uh, just learned it as, as one thing and not even bothered with runtime optimization. Why did you | 19.64 | 3.340468 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652889.mp3 | [
3106,
5723,
3496,
4088,
2734,
10414,
8493,
2892,
685,
11118,
5517,
10889,
6761,
10609,
3501,
2822,
8515,
12207,
9558,
10915,
3434,
5522,
4440,
12005,
12662,
11885,
6821,
6809,
4458,
3064,
10492,
8467,
5588,
12054,
12572,
400,
8677,
12278,
1... | [
0.8185270428657532,
-5.7657470703125,
-0.06964588165283203,
0.055917996913194656,
0.4193628430366516,
0.03934613615274429,
0.2387813925743103,
-0.013472999446094036,
-0.022922717034816742,
-3.714689254760742,
0.2835234999656677,
-0.022177862003445625,
0.14194782078266144,
0.084327965974807... | 491 | 19.64 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652890 | [S1] and this is going to lead me to, uh, good predictions. But, uh, this is only happens, you only can look at the effect at the very end of training, and then you're going to use that on validation. [S2] Mm-hmm. [S1] And so, uh, you could do that, and I think there's papers that do that using implicit gradients, um, but the signal is much, much, uh, more cumbersome. Um, instead, if you use, if you say, "Okay, no, uh, the way I'm optimizing this is inside the prediction function," then you can literally compute the grain, the, the, the computation graph and optimize it, and to it. | 29.68 | 2.82559 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652890.mp3 | [
3632,
8784,
4702,
10173,
5039,
9302,
7246,
3200,
3933,
12180,
6476,
977,
3918,
3906,
1749,
8996,
5815,
3358,
7382,
7188,
10888,
2980,
3732,
5900,
4998,
9304,
4244,
10902,
4996,
6176,
11009,
2869,
22,
87,
599,
534,
1179,
8817,
11301,
4031,... | [
0.679276704788208,
-5.700066566467285,
-0.016779586672782898,
-0.08445495367050171,
0.8063709139823914,
-0.06250432878732681,
0.313103586435318,
0.15463517606258392,
-0.1054757684469223,
-3.950892448425293,
0.4176570773124695,
-0.16354911029338837,
-0.32921481132507324,
-0.0107185160741209... | 742 | 29.68 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652891 | [S1] ... the ways to change the output conditioned on the input that, uh, kind of still do not, um, deviate too much from what it has learned. [S2] Mm-hmm. [S1] Uh, so theta captures the dynamics and says, "Okay, I probably got it a bit wrong because I'm not conserving G." Uh, so, but- [S2] Mm-hmm. [S1] ... but I don't want to deviate too much from what I've learned. So, uh, optimizing theta, still make sure that you're satisfied what you've learned so far, and then, and then it leads to much, much larger, uh, improvements. [S2] Yes. | 25.2 | 3.105202 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652891.mp3 | [
4137,
8280,
6732,
523,
5595,
9079,
2667,
5308,
11123,
11600,
11659,
6040,
3145,
8464,
5412,
4172,
12177,
9682,
12252,
11981,
9740,
2818,
281,
7364,
9174,
3453,
5950,
11062,
8907,
5728,
10537,
237,
3303,
4626,
5897,
9386,
5675,
9643,
5430,
... | [
0.6061731576919556,
-5.809365749359131,
-0.14145398139953613,
-0.1265614926815033,
0.7608469128608704,
-0.11323311924934387,
0.38745638728141785,
0.1073879525065422,
-0.11600266396999359,
-3.897937059402466,
0.37421494722366333,
-0.1293376237154007,
-0.2509937286376953,
-0.0410993658006191... | 630 | 25.2 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652892 | [S1] It, it's, it's, I think there's something like what you said that, that, that, that going to be, uh, there. Uh, in particular, it, it, I, I think it, G has a feeling like, uh, like this adversarial discriminator because it's telling you, "Oh, if you're not satisfying G conservation- [S2] Yeah. [S1] ... then most likely you are wrong." Especially if you don't satisfy it by a large amount. Because, again, they're, they're approximately conserved, so, um, that's one. Um, so one thing, uh, I'm, | 25.12 | 2.912322 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652892.mp3 | [
8232,
11356,
12180,
8203,
5825,
4244,
9044,
8908,
2569,
5570,
5512,
2957,
11102,
3998,
4956,
11875,
12069,
12172,
8202,
2753,
265,
4300,
11932,
12598,
11252,
11756,
9938,
8408,
12186,
11858,
6289,
8116,
8085,
4554,
3401,
3410,
5465,
8072,
5... | [
0.4954885244369507,
-5.853196620941162,
-0.07474453002214432,
-0.08247414231300354,
0.8160539269447327,
-0.015793561935424805,
0.374176949262619,
0.10357244312763214,
-0.1906626671552658,
-3.941352128982544,
0.26861968636512756,
-0.16720768809318542,
-0.26535215973854065,
-0.00352293974719... | 628 | 25.12 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652893 | [S1] ... interested in, uh, going forward, and, and I think that, that could be a, a venue of, of many future works, is that we focused a lot on when we were trying to make predictions on kind of generative, uh, networks. So the fact that you, sorry, generative not in the sense of self-supervised learning, but- [S2] Yeah. [S1] ... but more in, like, you predict the next input given the, the pre- the, sorry, the, the, the output given the input. So you have to generate the, the, the thing. | 24.44 | 3.111304 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652893.mp3 | [
5672,
5681,
12307,
12692,
11979,
11524,
4577,
5534,
5019,
3856,
5394,
12639,
5633,
8256,
3549,
11662,
11734,
9805,
8269,
8781,
5710,
4293,
6950,
6062,
5998,
3446,
3958,
4406,
4406,
3894,
4462,
1846,
9886,
9763,
3437,
7791,
3934,
9940,
7261,... | [
0.6688110828399658,
-5.806269645690918,
-0.1100572794675827,
-0.035200610756874084,
0.7366737127304077,
-0.1497323364019394,
0.36055871844291687,
0.09014707058668137,
-0.165283665060997,
-3.9384846687316895,
0.42196738719940186,
-0.16906088590621948,
-0.4072304666042328,
-0.079797074198722... | 611 | 24.44 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652894 | [S1] ... and code and, and construct maybe architecturally different from, from the F-networks. [S2] Mm-hmm. [S1] Uh, and maybe combining this, uh, proposal networks with this, uh, checking networks, uh, may, may make a different architecture classes that could be useful. | 14 | 3.190112 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652894.mp3 | [
8792,
12435,
8934,
8270,
9870,
6358,
3726,
4181,
2125,
4110,
9770,
8260,
7267,
9940,
12086,
9550,
9246,
8810,
10089,
8502,
10482,
7766,
3351,
9739,
8320,
2991,
7598,
7318,
8772,
4830,
7606,
8270,
12459,
3508,
9319,
7571,
3920,
9553,
5477,
... | [
0.9038700461387634,
-5.875949382781982,
-0.24904504418373108,
0.11993444710969925,
0.7119415402412415,
-0.1272292286157608,
0.4260317385196686,
0.14173874258995056,
-0.1981106996536255,
-3.9633781909942627,
0.34821978211402893,
-0.10712137818336487,
-0.447752445936203,
-0.10954824835062027... | 350 | 14 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652895 | [S1] Yeah, I wanted to get a little bit more into, so you have, you have experimental results where you compare to various baselines, like, you know, without, um, and, and, and obviously, obviously you're better than them, which is what we've come to expect from machine learning papers. [S2] [LAUGHS] [S1] I wanna, I wanna focus a little bit on also, here you have an investigation into what the, | 26.16 | 3.419096 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652895.mp3 | [
8745,
5201,
7370,
11213,
10674,
6519,
1461,
3688,
9266,
8747,
6526,
12205,
6349,
106,
4194,
11866,
8755,
9143,
2596,
7862,
7995,
11946,
10379,
11124,
9232,
10776,
8729,
8208,
8656,
5089,
11158,
7958,
7958,
5398,
1107,
6177,
9770,
5674,
1217... | [
0.8947421908378601,
-5.5941572189331055,
-0.006880355533212423,
-0.05736515298485756,
0.4877748191356659,
0.023617416620254517,
0.1917615830898285,
-0.0539386086165905,
-0.09207727015018463,
-3.766387701034546,
0.31037652492523193,
0.037830036133527756,
0.13041190803050995,
0.0654689371585... | 654 | 26.16 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652896 | [S1] We don't know. And the sixth one, we, uh, we found that it was following, uh, blue objects very closely. So here, of course, we only show, uh, one example over time. [S2] Mm-hmm. [S1] So this is a time sequence as we track the object. On, on the appendix, we, we show that they're, it basically didn't matter. The example didn't matter. It, it reproduced very nicely. And that also gave us confidence that the G-network was learning something meaningful. | 20.56 | 3.229515 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652896.mp3 | [
6696,
8216,
3243,
2854,
12623,
5393,
7935,
3310,
8270,
9045,
7988,
7917,
11988,
8865,
12385,
11827,
10329,
4684,
10038,
12511,
6211,
3053,
8495,
8980,
3561,
2768,
6105,
12621,
8802,
4314,
3937,
180,
7990,
11262,
9951,
11334,
9362,
4195,
109... | [
0.7469408512115479,
-5.853476524353027,
-0.14090795814990997,
-0.05596029385924339,
0.751301109790802,
-0.1364717334508896,
0.3977026045322418,
0.12452472746372223,
-0.13454671204090118,
-3.9666528701782227,
0.40852949023246765,
-0.1679067760705948,
-0.29268166422843933,
-0.037393745034933... | 514 | 20.56 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652897 | [S1] Everything is physics. If you're in the real world, um, like cars or people moving around. But, but they also, like, they also have some intrinsic mov- movement that not, doesn't follow. Passive physics loss, but, um, there's other- [S2] Do you have, do you have, like, something in mind? Like, except, except cuts between, you know, scenes. [S1] Yeah, cut, that cut, you, you'll get goodbye. [S2] Yeah. [S1] [LAUGHS] [S2] Do, do, do, do you have, do you have anything other in, is there, like, a prominent example- [S1] Yeah. [S2] ... where this type of model would, would fail? | 29.24 | 3.006374 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652897.mp3 | [
2608,
8792,
2142,
10166,
5934,
297,
9175,
7434,
3424,
4950,
7254,
9940,
2399,
3928,
4178,
6512,
4940,
7365,
264,
853,
4868,
6812,
1289,
723,
7595,
9194,
9638,
6882,
4184,
7006,
9492,
8866,
9958,
12468,
6893,
7387,
8786,
4309,
12622,
666,
... | [
0.6893365979194641,
-5.614926815032959,
0.042998090386390686,
-0.06723102182149887,
0.6423336267471313,
0.040243588387966156,
0.25403547286987305,
0.02138715796172619,
-0.1199512928724289,
-3.930209159851074,
0.27294161915779114,
-0.09119095653295517,
-0.05804353207349777,
0.06012815609574... | 731 | 29.24 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652899 | [S1] Uh, so, go ahead. [S2] One, one easy example of something that would fail is you have a video and you, uh, often have things that entered the video that were not in the video. [S1] Yeah. [S2] Um, then here you get into trouble because there's a, something that was not observed. It's the same thing that we were talking energy dissipation before. If you con- and consider the entire system, then maybe there's something that's going to get conserved if you consider heat and whatnot. [S1] Yeah. [S2] But anything that you cannot observe, then forces to- some things that are not getting observed. Um, so, yeah, extra objects, um, | 29.24 | 3.159605 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652899.mp3 | [
3958,
4342,
6492,
10748,
10742,
7022,
8093,
2870,
3892,
6964,
6443,
3817,
11354,
1455,
3373,
4380,
4331,
7925,
7989,
12725,
12579,
10827,
11412,
10894,
8917,
4316,
9902,
2669,
8054,
11260,
8913,
10898,
8788,
4235,
11370,
12325,
12620,
11660,
... | [
0.4923214316368103,
-5.820369720458984,
0.04222998023033142,
-0.08360499143600464,
0.8046947717666626,
-0.1394883096218109,
0.3521970510482788,
0.12299881130456924,
-0.287580668926239,
-3.9160878658294678,
0.2971951961517334,
-0.1567407250404358,
-0.3573246896266937,
0.04400205612182617,
... | 731 | 29.24 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652900 | [S1] Yes, yes, exactly. So, yeah, things, and one other thing, I think, conversely, it could be that there's a lot of work that will need to be done if the camera is, uh, is, uh, moving a lot, um, because- [S2] And then- [S1] ... all of these objects will for sure appear that were not there, because you're looking at stuff that was not there. Uh, so if you look at the videos, this video is static. | 21.72 | 3.152182 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652900.mp3 | [
6747,
11283,
11819,
12315,
8738,
7180,
6859,
11724,
10677,
6583,
1428,
5456,
833,
1090,
4372,
11726,
4023,
4372,
1481,
4463,
12444,
1410,
8054,
2422,
12058,
4451,
5844,
5020,
8528,
5604,
10540,
10414,
5342,
7903,
661,
8801,
9249,
8361,
7261... | [
0.6294112205505371,
-5.844748497009277,
-0.09993685036897659,
-0.04443734511733055,
0.6771799325942993,
-0.14522503316402435,
0.34605643153190613,
0.07604585587978363,
-0.1143597960472107,
-3.979724884033203,
0.4002266228199005,
-0.15539821982383728,
-0.2601327896118164,
-0.046978581696748... | 543 | 21.72 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652901 | [S1] But it's, I mean, just, just out of intuition, it seems more likely that the network detects something like, you know, there's, there's a blue bunch of pixels and, and, uh, an orange bunch of pixels, and these pixels sort of move together as objects. [S2] Yeah. [S1] Rather than the network from video somehow determining, "Aha, there's laws of physics and there's gravity and there's friction and there's sliding." It, the first situation seems a bit more likely here, right? [S2] Yes, yes. | 28.48 | 3.357961 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652901.mp3 | [
8233,
5162,
12115,
6811,
1571,
10537,
10685,
11502,
5136,
9824,
10927,
12245,
9225,
2962,
3041,
832,
9379,
8370,
9257,
6704,
10927,
6142,
6686,
11677,
12675,
9286,
5764,
4268,
9958,
9974,
8820,
6886,
4314,
7200,
8273,
12673,
10717,
10619,
9... | [
0.6319044828414917,
-5.533860206604004,
0.08225808292627335,
-0.13259358704090118,
0.4269329607486725,
-0.0019388390937820077,
0.2652531862258911,
-0.02685299701988697,
-0.13872471451759338,
-3.815322160720825,
0.3156309425830841,
0.01258399710059166,
0.06842222064733505,
0.098022297024726... | 712 | 28.48 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652902 | [S1] Sure. Uh, I didn't know exactly how. And then, um, the, Ross DeDray gave a talk at MIT, uh, it's online on the YouTube, uh, seminar. Uh, and he was talk- telling us how, um, it's very hard to encode inductive biases in neural networks. And in their case, basically, they were predicting how a robot was pushing a bunch of carrot, and the carrot was moving around, and they trained it- [S2] Mm-hmm. [S1] ... they trained a carrot predictor, uh, | 27 | 3.271325 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652902.mp3 | [
6184,
4170,
4825,
7521,
10073,
10201,
5558,
2982,
460,
778,
6306,
9825,
7209,
9876,
8878,
3519,
886,
886,
886,
4966,
12534,
6142,
12686,
5826,
11615,
9365,
5406,
5718,
11342,
3447,
5414,
4822,
10012,
970,
8638,
4910,
12570,
3868,
4878,
39... | [
0.5178981423377991,
-5.885233402252197,
0.004738064482808113,
-0.12374090403318405,
0.675106942653656,
-0.06685514748096466,
0.3644954562187195,
0.1357923001050949,
-0.12221521139144897,
-3.9031918048858643,
0.28747910261154175,
-0.2730565071105957,
-0.31297799944877625,
0.0156395714730024... | 675 | 27 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652903 | [S1] Cool. Is there anything you, else you want to say about the, the experimental results? We touched on sort of upping the inner steps and the, and the, uh, the grad chem, but is there anything- [S2] We- [S1] ... special you want to say about sort of your, your tests on, uh, for example, the pendulums or- | 17.52 | 3.381934 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652903.mp3 | [
11922,
7345,
11476,
5033,
5221,
7819,
10466,
5221,
3154,
7211,
8801,
6805,
12534,
3181,
4716,
3306,
7200,
7729,
5633,
4646,
12701,
3529,
3009,
10739,
8092,
8164,
11996,
11611,
9539,
6568,
2992,
12180,
11797,
9940,
11716,
7837,
7894,
3295,
3... | [
0.8053982853889465,
-5.765412330627441,
-0.01784892939031124,
0.016501566395163536,
0.41396841406822205,
0.025999844074249268,
0.16099964082241058,
-0.09079333394765854,
-0.05853462964296341,
-3.7169065475463867,
0.26951244473457336,
-0.068867526948452,
0.030464189127087593,
0.008943463675... | 438 | 17.52 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652904 | [S1] Yeah, I think some of the experiments, uh, depends on the, how much time we have, but on the, on the pendulum, there was a symbolic component, so the, the G doesn't have to be fully neural. [S2] Yeah. [S1] Uh, so in, in the origin, in the first, I think those are the first experiment, the G is kind of a program with some parameter. [S2] Yeah. [S1] Uh, like a formula, and there we search over formulas, uh, because it's a state information, the pendulum that you draw, like the angle and the momentum. | 26.16 | 3.166793 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652904.mp3 | [
9810,
3589,
6402,
11219,
8114,
11244,
10004,
6560,
2856,
11617,
11600,
11355,
11870,
4749,
6225,
9298,
7371,
3611,
1170,
4064,
3545,
7990,
11437,
7779,
8500,
5219,
81,
2980,
11110,
11110,
5991,
5998,
5999,
5999,
5487,
6070,
1820,
6818,
9377... | [
0.5962957143783569,
-5.826827049255371,
-0.009019976481795311,
-0.054559897631406784,
0.6371845006942749,
-0.06325756013393402,
0.35271942615509033,
0.1084279865026474,
-0.170784592628479,
-3.8984322547912598,
0.32036352157592773,
-0.21627023816108704,
-0.346279501914978,
0.017285477370023... | 654 | 26.16 |
bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652905 | [S1] And there you, we search over formulas, um, and then there's some parameters as well that get trained over, uh, with gray in the center. [S2] Yeah. [S1] And there we saw that, okay, we, we are able to recover the true formulas of the energy and it leads to better prediction than a vanilla MLP that does not learn about- [S2] Yeah. [S1] ... observations. Um- [S2] And there also you can see that, that actually you can, you can even handle these approximate constraints where you have real data which then the networks that have the hard-coded constraints can't handle as well. | 29.88 | 3.230198 | 24,000 | audio/en/bilibili_data_1624174493_BV17h4y1j7aZ_p164_BV17h4y1j7aZ_p164_m4-dialogue_0652905.mp3 | [
3691,
9361,
4837,
4975,
5647,
3650,
9709,
11251,
5533,
6859,
9484,
3212,
2740,
2676,
10118,
5088,
8528,
3547,
7982,
2885,
8330,
12689,
8038,
7782,
2675,
8494,
3459,
7408,
5498,
756,
8311,
5523,
8284,
5236,
8932,
8467,
11122,
11562,
4522,
... | [
0.6508334875106812,
-5.683277130126953,
0.07212638109922409,
-0.19087669253349304,
0.5142034292221069,
-0.032329898327589035,
0.2462475746870041,
0.02678028494119644,
-0.190087229013443,
-3.8605897426605225,
0.28105399012565613,
-0.15421684086322784,
-0.07265264540910721,
0.103970505297183... | 747 | 29.88 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.